spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:23:26.580Z","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:23:26.585Z","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}4{"parentUuid":null,"isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":"Begin"},"uuid":"e646bf3d-19bb-47b1-8f34-62a23446b6c3","timestamp":"2026-10-30T05:23:26.590Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","timestamp":"2026-10-30T05:23:26.605Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0282\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}12{"parentUuid":"fff69a45-f9b3-4829-85e2-37460a2d9c77","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs1HKYwJpnC6BzVA2sx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019ZqssRycXNrG1AZMgmiu3Q","name":"Bash","input":{"command":"cat TASK.md; 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standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 40\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  159 simon-pierreboucher  staff  5088 oct.  30 01:23 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\ndata:\ntotal 8\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  1822 oct.  29 23:45 README.md\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 sources","is_error":true,"tool_use_id":"toolu_019ZqssRycXNrG1AZMgmiu3Q"}]},"uuid":"c4b7ba1c-113f-42e5-9b86-a4d998eb03f5","timestamp":"2026-10-30T05:23:29.100Z","toolUseResult":"Error: Exit code 1\n# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 40\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:45 .\ndrwxr-xr-x  159 simon-pierreboucher  staff  5088 oct.  30 01:23 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\ndata:\ntotal 8\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 .\ndrwxr-xr-x  6 simon-pierreboucher  staff   192 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  1822 oct.  29 23:45 README.md\ndrwxr-xr-x  4 simon-pierreboucher  staff   128 oct.  29 23:45 sources","sandboxed":false,"sourceToolAssistantUUID":"66b06975-6524-42a2-9959-1dfa000621eb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"da58e6eed4a70e8f85192562436fd8aeb43bd083c99651b47b857734877c9b29","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_019ZqssRycXNrG1AZMgmiu3Q","name":"Bash","input":{"command":"cat TASK.md; 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cat gate_spec.json bundle.json; wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3749,"cache_read_input_tokens":4107,"output_tokens":238,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3749},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs1QuxRnwX5HnC2AisV","requestRef":"e1d4d34e-be67-4f45-a2d8-a25a9846a0f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"372301e3-1176-44d6-bef3-7a9475a97e65","timestamp":"2026-10-30T05:23:31.293Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}19{"parentUuid":"372301e3-1176-44d6-bef3-7a9475a97e65","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0121Ni3Z1ymrCSziMxoR49Br","type":"tool_result","content":"# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_5km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_obs_5km\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_fatal_att_1km\": 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Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. 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    1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"6705d198-3f42-4980-8f9f-29b3d2e79be9","timestamp":"2026-10-30T05:23:31.587Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"88fa93ee-86e8-42db-a422-9267e639fe4b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}22{"parentUuid":"6705d198-3f42-4980-8f9f-29b3d2e79be9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs1RCKUkvwE5jckhMac","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01U9qnY92vRWRqiMFkT3zhEx","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3749,"cache_read_input_tokens":4107,"output_tokens":238,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3749},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXs1QuxRnwX5HnC2AisV","requestRef":"e1d4d34e-be67-4f45-a2d8-a25a9846a0f5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"18f9cc47-3a71-4d66-8ae1-2387cf0e80e2","timestamp":"2026-10-30T05:23:31.898Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"18f9cc47-3a71-4d66-8ae1-2387cf0e80e2","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01U9qnY92vRWRqiMFkT3zhEx","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model","is_error":false}]},"uuid":"6ce9389d-e2a6-4aab-8794-63c5cf97614c","timestamp":"2026-10-30T05:23:31.922Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"18f9cc47-3a71-4d66-8ae1-2387cf0e80e2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}24{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"ca0cd1d3071506d1a9065264b5b51c188941751f2a7dc55dcdc0903b4448556f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0121Ni3Z1ymrCSziMxoR49Br","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json bundle.json; wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*"}},{"type":"tool_use","id":"toolu_012dRRd2AkXdjKRNqGQcV7ip","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2609.27732v1.txt"}},{"type":"tool_use","id":"toolu_01U9qnY92vRWRqiMFkT3zhEx","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt"}}]}}25{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"fd99e90e26e7bcce0aab880e8e6b43d92ed03bf5e96c2667403bb7c0765353e3","message":{"role":"user","content":[{"tool_use_id":"toolu_0121Ni3Z1ymrCSziMxoR49Br","type":"tool_result","content":"# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_5km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_obs_5km\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_fatal_att_1km\": 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\"F4-conf-01\",\n  \"family\": \"F4\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:45:06.348580+00:00\",\n  \"files\": {\n    \"TASK.md\": \"c0ca541d98a4fc46ff5a6dd27fc6e09ae1875e5a5ce7c2a8d963f80138352d20\",\n    \"data/README.md\": \"16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929\",\n    \"data/sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\",\n    \"data/sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n    \"gate_spec.json\": \"eb563a002112a161b73ac3c4c1f83feafc9ff733f35db10c13f1c95503ea6fc5\"\n  }\n}      10 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     202 total\n16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929  data/README.md\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238  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Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_01U9qnY92vRWRqiMFkT3zhEx","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression 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Derived quantities are meaningful under an approximate normal sampling distribution of the estimator (CI = est ± 1.96·SE; SE = (hi − lo)/(2·1.96)); the RESEA CI is described as from \"the methodology described in the main text\" (not in extract), so symmetric-normal is an assumption.\n\n**Outputs**: out/results.json (keys listed in TASK.md: dcr_* , ri_*, locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (6 sections), out/analysis.py, out/claims.json, plus protocol state files and out/exclusions.csv.\n\n**Information rules**: only the two extracts are evidence; values as printed (sign, decimals) from the table named in the key; derived keys from the named table with stated convention; non-printed quantity → null with \"not reported in <source>\" locator.\n\n**Invalidity**: using values from another table (e.g. Table A19 SE 0.218 or Table 3), rounding/rescaling printed values, inventing a p-value, or using memory of the papers.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md"}}]}}33{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"76fbb34e-a511-4378-a644-b3d2a2904344","timestamp":"2026-10-30T05:23:51.672Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"b4904d26-eef0-4935-b84e-d9c8818ec489","keep":6,"tail":["88807e610ef098d2568d2408c2bd5ade0e5eeeedb8515a6ee9b5ccf895b9bf85","624aab7f070355d68f603e7832956de7237b240f19d02bede35bd0fe990f086e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"67408df78fa14f3aa7319355ce3efa869d01459e75e93365e5f14cb3c94a6de6"}34{"parentUuid":"9171923e-c94e-45f7-b5c4-448089f947ce","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs35fdjJUZHBTYthtma","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CkJofPduF7fPsB6DmUoT2t","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | lines (wc -l) | SHA-256 | matches README/bundle |\n|---|---|---|---|\n| data/README.md | 10 | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | 134 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | yes |\n| data/sources/arxiv-2606.14621v1.txt | 58 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR tables: coefficient row followed (after a blank line) by an SE row in parentheses; columns 1 km / 3 km / 5 km. Units: log points (ATT), counts (regions, region-years). RESEA Table 2 (re-set by extract editor): 5 columns, cells hold \"estimate; p=..; 95% CI: [lo, hi]\"; USD/yr, pp, weeks. No dates beyond submission/retrieval (2026). No missing cells in the requested rows; no duplicates.\n\n## Anomalies / internal inconsistencies (handled in results.json `data_issues`)\n1. DCR 5 km CS-DR ATT −0.431 has SE (0.210) in Table 6 but (0.218) in Table A19. Keys name Table 6 → use 0.210.\n2. Table A19 note says 640 never-treated comparison regions; Tables 6/7 and Figure 6 note say 608. Not used for any key; reported.\n3. Abstract says fatal hospitalisations \"fall by 35%\"; Section 6.1 says \"approximately 30% at 5 km\"; Table 7 5 km fatal ATT −0.354 ⇒ exp(−0.354)−1 ≈ −29.8%. Not used; reported.\n4. Text says \"HIV estimates are insignificant and close to zero\" though Table 7 5 km HIV ATT is −0.340 (SE 0.294); insignificant is consistent with no star; \"close to zero\" is the author's wording. Reported only.\n5. Figure 7 note / Table 7 note list four sub-categories while Table 7 has five rows (fatal not given an ICD code). Reported only.\n6. RESEA text reports weeks-on-UI CI as positive magnitudes [1.75, 2.24] (a reduction), Table 2 prints [-2.24, -1.75]. Keys use Table 2.\n7. RESEA text contains a rendering defect \"p ¡ 0.001\" (should read p < 0.001). No key affected.\n8. RESEA ATE wage CI [166, 2,141] is near-symmetric around 1,153 (987 vs 988), consistent with rounding.\n9. The exact two-sided p-value of the HIV 5 km ATT is not printed anywhere (only star convention) → null.\n\n## Availability\nBoth extracts are the admissible evidence; nothing violates the rules. README.md read for provenance only. Table 3 (RESEA) and Table A19 (DCR) duplicate some values; keys name Tables 2 and 6, which are used.\n\n## Exclusions\nNo rows excluded; the \"analysis units\" are the 26 requested reported items (see exclusions.csv).\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,requested items located in named tables (no exclusions),26,26,0,All 26 requested value/boolean items were assessed; one (HIV 5 km p-value) is not printed and is reported as null rather than excluded\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: audit + hashes verified; Table 6 SE preferred over A19 SE.\" >> state/log.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1770,"cache_read_input_tokens":21296,"output_tokens":1411,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1770},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs35NGujpYrgBcji3d6","requestRef":"76fbb34e-a511-4378-a644-b3d2a2904344","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"df533b75-0df1-4146-b7b4-191a8a26e1ec","timestamp":"2026-10-30T05:24:06.770Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282/out/state","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"df533b75-0df1-4146-b7b4-191a8a26e1ec","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CkJofPduF7fPsB6DmUoT2t","type":"tool_result","content":"(eval):cd:1: no such file or directory: out/state","is_error":false}]},"uuid":"f02cbbec-d648-4dad-b1d9-6149eaa6b27e","timestamp":"2026-10-30T05:24:06.794Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out/state","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"df533b75-0df1-4146-b7b4-191a8a26e1ec","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282/out","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"5497839e2dc961ba9bce918e46679e1c1532a218d941e98099438ca12ed27cea","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01CkJofPduF7fPsB6DmUoT2t","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | lines (wc -l) | SHA-256 | matches README/bundle |\n|---|---|---|---|\n| data/README.md | 10 | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | 134 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | yes |\n| data/sources/arxiv-2606.14621v1.txt | 58 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR tables: coefficient row followed (after a blank line) by an SE row in parentheses; columns 1 km / 3 km / 5 km. Units: log points (ATT), counts (regions, region-years). RESEA Table 2 (re-set by extract editor): 5 columns, cells hold \"estimate; p=..; 95% CI: [lo, hi]\"; USD/yr, pp, weeks. No dates beyond submission/retrieval (2026). No missing cells in the requested rows; no duplicates.\n\n## Anomalies / internal inconsistencies (handled in results.json `data_issues`)\n1. DCR 5 km CS-DR ATT −0.431 has SE (0.210) in Table 6 but (0.218) in Table A19. Keys name Table 6 → use 0.210.\n2. Table A19 note says 640 never-treated comparison regions; Tables 6/7 and Figure 6 note say 608. Not used for any key; reported.\n3. Abstract says fatal hospitalisations \"fall by 35%\"; Section 6.1 says \"approximately 30% at 5 km\"; Table 7 5 km fatal ATT −0.354 ⇒ exp(−0.354)−1 ≈ −29.8%. Not used; reported.\n4. Text says \"HIV estimates are insignificant and close to zero\" though Table 7 5 km HIV ATT is −0.340 (SE 0.294); insignificant is consistent with no star; \"close to zero\" is the author's wording. Reported only.\n5. Figure 7 note / Table 7 note list four sub-categories while Table 7 has five rows (fatal not given an ICD code). Reported only.\n6. RESEA text reports weeks-on-UI CI as positive magnitudes [1.75, 2.24] (a reduction), Table 2 prints [-2.24, -1.75]. Keys use Table 2.\n7. RESEA text contains a rendering defect \"p ¡ 0.001\" (should read p < 0.001). No key affected.\n8. RESEA ATE wage CI [166, 2,141] is near-symmetric around 1,153 (987 vs 988), consistent with rounding.\n9. The exact two-sided p-value of the HIV 5 km ATT is not printed anywhere (only star convention) → null.\n\n## Availability\nBoth extracts are the admissible evidence; nothing violates the rules. README.md read for provenance only. Table 3 (RESEA) and Table A19 (DCR) duplicate some values; keys name Tables 2 and 6, which are used.\n\n## Exclusions\nNo rows excluded; the \"analysis units\" are the 26 requested reported items (see exclusions.csv).\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,requested items located in named tables (no exclusions),26,26,0,All 26 requested value/boolean items were assessed; one (HIV 5 km p-value) is not printed and is reported as null rather than excluded\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: audit + hashes verified; Table 6 SE preferred over A19 SE.\" >> state/log.md"}}]}}37{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"a284c7f9-f5e6-44d3-9bae-98be937dcee1","timestamp":"2026-10-30T05:24:06.799Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"76fbb34e-a511-4378-a644-b3d2a2904344","keep":8,"tail":["5497839e2dc961ba9bce918e46679e1c1532a218d941e98099438ca12ed27cea","f35f9c83f42dadec5ece7339b39551c59fdf5d7e333c178d54f26bc9f8aebee4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"418486d37cca61a8bdc35b2d811fc8204c381e6c077cbf57fa1cb8be087c6223"}38{"parentUuid":"f02cbbec-d648-4dad-b1d9-6149eaa6b27e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs4CHm5euCy4H75PRds","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1435,"cache_read_input_tokens":23066,"output_tokens":173,"output_tokens_details":{"thinking_tokens":91},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1435},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":740,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs4C2tKPHzKCwMwwtd4","requestRef":"a284c7f9-f5e6-44d3-9bae-98be937dcee1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"99262e79-9371-4534-a9a7-0890b8d9111b","timestamp":"2026-10-30T05:24:09.149Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282/out","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"99262e79-9371-4534-a9a7-0890b8d9111b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs4CHm5euCy4H75PRds","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JRHLqTwJHmRQBGAh2wCP9P","name":"Bash","input":{"command":"pwd; 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tables one row per line with \" | \" separators. DCR tables: coefficient row followed (after a blank line) by an SE row in parentheses; columns 1 km / 3 km / 5 km. Units: log points (ATT), counts (regions, region-years). RESEA Table 2 (re-set by extract editor): 5 columns, cells hold \"estimate; p=..; 95% CI: [lo, hi]\"; USD/yr, pp, weeks. No dates beyond submission/retrieval (2026). No missing cells in the requested rows; no duplicates.\n\n## Anomalies / internal inconsistencies (handled in results.json `data_issues`)\n1. DCR 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19. Keys name Table 6 -> use 0.210.\n2. Table A19 note says 640 never-treated comparison regions; Tables 6/7 and Figure 6 note say 608. Not used for any key; reported.\n3. Abstract says fatal hospitalisations \"fall by 35%\"; Section 6.1 says \"approximately 30% at 5 km\"; Table 7 5 km fatal ATT -0.354 => exp(-0.354)-1 ~ -29.8%. Not used; reported.\n4. Text says \"HIV estimates are insignificant and close to zero\" though Table 7 5 km HIV ATT is -0.340 (SE 0.294); insignificance is consistent with no star; \"close to zero\" is the author's wording. Reported only.\n5. Figure 7 note / Table 7 note list four sub-categories while Table 7 has five outcome rows. Reported only.\n6. RESEA text reports the weeks-on-UI CI as positive magnitudes [1.75, 2.24] (a reduction); Table 2 prints [-2.24, -1.75]. Keys use Table 2.\n7. RESEA text contains a rendering defect \"p ¡ 0.001\" (should read p < 0.001). No key affected.\n8. RESEA wage ATE CI [166, 2,141] is near-symmetric around 1,153 (987 vs 988), consistent with rounding.\n9. The exact two-sided p-value of the HIV 5 km ATT is not printed anywhere (only star convention) -> null.\n\n## Availability\nBoth extracts are admissible evidence; nothing violates the rules. README.md read for provenance only. RESEA Table 3 and DCR Table A19 duplicate some values; keys name Tables 2 and 6, which are used.\n\n## Exclusions\nNo rows excluded; the analysis units are the 26 requested reported items (see out/exclusions.csv).\nEOF\necho \"- 2026-10-30 P1 passed: audit + hashes verified; Table 6 SE preferred over A19 SE.\" >> out/state/log.md; cat out/exclusions.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":260,"cache_read_input_tokens":24501,"output_tokens":1324,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":260},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs4QSGGq1wP77G8Jh1E","requestRef":"cef05170-a3de-4133-bcdd-bb73e58a9990","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0e0a78ec-ae36-4bda-9d3c-9b9c6a454348","timestamp":"2026-10-30T05:24:21.445Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282/out","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"0e0a78ec-ae36-4bda-9d3c-9b9c6a454348","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T2RB3QTpuQUc4ySbsP7iMf","type":"tool_result","content":"step,rule,n_before,n_after,n_removed,rationale\n1,requested items located in named tables (no exclusions),26,26,0,All 26 requested value/boolean items were assessed; one (HIV 5 km p-value) is not printed and is reported as null rather than excluded","is_error":false}]},"uuid":"43f24111-b41b-4fd6-ae3f-ef0414e0adfe","timestamp":"2026-10-30T05:24:21.471Z","toolUseResult":{"stdout":"step,rule,n_before,n_after,n_removed,rationale\n1,requested items located in named tables (no exclusions),26,26,0,All 26 requested value/boolean items were assessed; one (HIV 5 km p-value) is not printed and is reported as null rather than excluded","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0e0a78ec-ae36-4bda-9d3c-9b9c6a454348","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"dc41a4cfe1a938bcd3aa1d597fffc7f9411f9f636c82e8304c7d6cb39b14fa9a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01T2RB3QTpuQUc4ySbsP7iMf","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0282 && cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | lines (wc -l) | SHA-256 | matches README/bundle |\n|---|---|---|---|\n| data/README.md | 10 | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | 134 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | yes |\n| data/sources/arxiv-2606.14621v1.txt | 58 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR tables: coefficient row followed (after a blank line) by an SE row in parentheses; columns 1 km / 3 km / 5 km. Units: log points (ATT), counts (regions, region-years). RESEA Table 2 (re-set by extract editor): 5 columns, cells hold \"estimate; p=..; 95% CI: [lo, hi]\"; USD/yr, pp, weeks. No dates beyond submission/retrieval (2026). No missing cells in the requested rows; no duplicates.\n\n## Anomalies / internal inconsistencies (handled in results.json `data_issues`)\n1. DCR 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but (0.218) in Table A19. Keys name Table 6 -> use 0.210.\n2. Table A19 note says 640 never-treated comparison regions; Tables 6/7 and Figure 6 note say 608. Not used for any key; reported.\n3. Abstract says fatal hospitalisations \"fall by 35%\"; Section 6.1 says \"approximately 30% at 5 km\"; Table 7 5 km fatal ATT -0.354 => exp(-0.354)-1 ~ -29.8%. Not used; reported.\n4. Text says \"HIV estimates are insignificant and close to zero\" though Table 7 5 km HIV ATT is -0.340 (SE 0.294); insignificance is consistent with no star; \"close to zero\" is the author's wording. Reported only.\n5. Figure 7 note / Table 7 note list four sub-categories while Table 7 has five outcome rows. Reported only.\n6. RESEA text reports the weeks-on-UI CI as positive magnitudes [1.75, 2.24] (a reduction); Table 2 prints [-2.24, -1.75]. Keys use Table 2.\n7. RESEA text contains a rendering defect \"p ¡ 0.001\" (should read p < 0.001). No key affected.\n8. RESEA wage ATE CI [166, 2,141] is near-symmetric around 1,153 (987 vs 988), consistent with rounding.\n9. The exact two-sided p-value of the HIV 5 km ATT is not printed anywhere (only star convention) -> null.\n\n## Availability\nBoth extracts are admissible evidence; nothing violates the rules. README.md read for provenance only. RESEA Table 3 and DCR Table A19 duplicate some values; keys name Tables 2 and 6, which are used.\n\n## Exclusions\nNo rows excluded; the analysis units are the 26 requested reported items (see out/exclusions.csv).\nEOF\necho \"- 2026-10-30 P1 passed: audit + hashes verified; Table 6 SE preferred over A19 SE.\" >> out/state/log.md; cat out/exclusions.csv"}}]}}48{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"17a03b69-5c29-45ab-9136-c95893a0c85b","timestamp":"2026-10-30T05:24:21.474Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"cef05170-a3de-4133-bcdd-bb73e58a9990","keep":12,"tail":["dc41a4cfe1a938bcd3aa1d597fffc7f9411f9f636c82e8304c7d6cb39b14fa9a","42b194c410270022282f50d2fe87872c238fd301eb8db4de6ece4d97623cb320"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"630d08ec28f277378db047d6c6edcb46b553a7f096f23c5307aa7552c7aefd0c"}49{"parentUuid":"43f24111-b41b-4fd6-ae3f-ef0414e0adfe","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs5H5XXoPCPGATL6Ffn","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1420,"cache_read_input_tokens":24761,"output_tokens":5527,"output_tokens_details":{"thinking_tokens":319},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1420},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":3634,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs5GoA8S9Nps93wfLxA","requestRef":"17a03b69-5c29-45ab-9136-c95893a0c85b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a286485c-fec9-43fb-a361-be5f57327be5","timestamp":"2026-10-30T05:24:32.622Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"a286485c-fec9-43fb-a361-be5f57327be5","isSidechain"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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed estimates from two arXiv extracts and derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nDCR_F = os.path.join(ROOT, \"data\", \"sources\", DCR_ID + \".txt\")\nRI_F = os.path.join(ROOT, \"data\", \"sources\", RI_ID + \".txt\")\nREADME_F = os.path.join(ROOT, \"data\", \"README.md\")\nZ = 1.96  # normal critical value for two-sided 95%\n\ndef read(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read()\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\ndef num(s):\n    \"\"\"Parse a printed number string (keep sign; strip $, commas, stars, %, units).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\")\n    m = re.search(r\"-?\\d+(?:\\.\\d+)?\", s)\n    return m.group(0)\n\ndef to_val(s):\n    return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef table_block(text, title_prefix):\n    lines = text.splitlines()\n    start = next(i for i, l in enumerate(lines) if l.startswith(title_prefix))\n    block = []\n    for l in lines[start + 1:]:\n        if l.startswith(\"Note:\"):\n            break\n        if l.strip():\n            block.append(l)\n    return block\n\ndef parse_reg_table(block):\n    \"\"\"Return header cols, {row_label: {col: (coef_str, se_str or None, raw lines)}}.\"\"\"\n    header = [c.strip() for c in block[0].split(\"|\")]\n    cols = [c for c in header if c and c != \"Outcome\"]\n    rows, last = {}, None\n    for l in block[1:]:\n        cells = [c.strip() for c in l.split(\"|\")]\n        if cells[0] == \"\" and last is not None and all(c.startswith(\"(\") for c in cells[1:] if c):\n            for c, v in zip(cols, cells[1:]):\n                coef, _, raw = rows[last][c]\n                rows[last][c] = (coef, v, raw + [l])\n            continue\n        label = cells[0]\n        rows[label] = {c: (v, None, [l]) for c, v in zip(cols, cells[1:])}\n        last = label\n    return cols, rows\n\ndcr = read(DCR_F); ri = read(RI_F)\nt6_cols, t6 = parse_reg_table(table_block(dcr, \"Table 6:\"))\nt7_cols, t7 = parse_reg_table(table_block(dcr, \"Table 7:\"))\n\nR, LOC, PRINTED, MATCHED = {}, {}, {}, {}\n\ndef loc(src, tab, row, col, extra=\"\"):\n    return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\ndef put_dcr(key, tab, rows, row, col, what):\n    coef, se, raw = rows[row][col]\n    s = coef if what == \"coef\" else se\n    p = num(s)\n    R[key] = to_val(p); PRINTED[key] = s\n    LOC[key] = loc(DCR_ID, tab, row, col, \"\" if what == \"coef\" else \" (standard error in parentheses on the line below)\")\n    MATCHED[key] = raw\n    return coef\n\nput_dcr(\"dcr_att_5km\", 6, t6, \"ATT\", \"5 km\", \"coef\")\nput_dcr(\"dcr_att_5km_se\", 6, t6, \"ATT\", \"5 km\", \"se\")\nput_dcr(\"dcr_att_3km\", 6, t6, \"ATT\", \"3 km\", \"coef\")\nput_dcr(\"dcr_att_3km_se\", 6, t6, \"ATT\", \"3 km\", \"se\")\nput_dcr(\"dcr_obs_5km\", 6, t6, \"Observations\", \"5 km\", \"coef\")\nput_dcr(\"dcr_narcotics_att_3km\", 7, t7, \"Narcotics\", \"3 km\", \"coef\")\nput_dcr(\"dcr_narcotics_att_3km_se\", 7, t7, \"Narcotics\", \"3 km\", \"se\")\nput_dcr(\"dcr_fatal_att_1km\", 7, t7, \"Drug-related (fatal)\", \"1 km\", \"coef\")\nput_dcr(\"dcr_fatal_att_1km_se\", 7, t7, \"Drug-related (fatal)\", \"1 km\", \"se\")\nhiv_coef = put_dcr(\"dcr_hiv_att_5km\", 7, t7, \"HIV\", \"5 km\", \"coef\")\nput_dcr(\"dcr_hiv_att_5km_se\", 7, t7, \"HIV\", \"5 km\", \"se\")\n\n# HIV exact p-value: search the extract for any printed p-value (p= / p-value) -- none expected\npv_hits = [l for l in dcr.splitlines() if re.search(r\"p\\s*=\\s*0?\\.\\d|p-value\", l, re.I)]\nR[\"dcr_hiv_att_5km_p_value\"] = None\nLOC[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE and \"\n                                  f\"significance stars; {len(pv_hits)} exact p-value strings found in the extract)\")\nstars = len(hiv_coef) - len(hiv_coef.rstrip(\"*\"))\nR[\"dcr_hiv_att_5km_significant_10pct\"] = stars >= 1  # * p<0.10 or stronger\n\n# Derived 95% CI from Table 6 printed values\nb, se = R[\"dcr_att_5km\"], R[\"dcr_att_5km_se\"]\nR[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 6)\nR[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 6)\nfor k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n    LOC[k] = (f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE')\n\n# RESEA Table 2\nlines = ri.splitlines()\nhdr_i = next(i for i, l in enumerate(lines) if l.startswith(\"Outcome | Treatment | Control\"))\nt2_cols = [c.strip() for c in lines[hdr_i].split(\"|\")]\nt2 = {}\nfor l in lines[hdr_i + 1:]:\n    if not l.strip():\n        break\n    cells = [c.strip() for c in l.split(\"|\")]\n    t2[cells[0]] = dict(zip(t2_cols[1:], cells[1:])) | {\"_raw\": l}\nATE = \"Average treatment effect estimate\"\n\ndef parse_effect(cell):\n    est = cell.split(\";\")[0]\n    m = re.search(r\"95% CI:\\s*\\[([^\\]]+)\\]\", cell)\n    lo, hi = [x.strip() for x in m.group(1).split(\", \")]\n    return est, lo, hi\n\ndef put_ri(key, row, col, s, part):\n    R[key] = to_val(num(s)); PRINTED[key] = s\n    LOC[key] = loc(RI_ID, 2, row, col, part); MATCHED[key] = [t2[row][\"_raw\"]]\n\nw_est, w_lo, w_hi = parse_effect(t2[\"Wage\"][ATE])\nput_ri(\"ri_wage_ate\", \"Wage\", ATE, w_est, \" (point estimate)\")\nput_ri(\"ri_wage_ate_ci_low\", \"Wage\", ATE, w_lo, \" (95% CI lower bound)\")\nput_ri(\"ri_wage_ate_ci_high\", \"Wage\", ATE, w_hi, \" (95% CI upper bound)\")\nR[\"ri_wage_ate_se_reported\"] = (\"SE\" in t2[\"Wage\"][ATE].replace(\"SD\", \"\")) or (\"standard error\" in ri.lower())\nR[\"ri_wage_ate_se_derived\"] = round((R[\"ri_wage_ate_ci_high\"] - R[\"ri_wage_ate_ci_low\"]) / (2 * Z), 6)\nLOC[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived from printed 95% CI bounds')\nctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0]\nput_ri(\"ri_control_mean_wage\", \"Wage\", \"Control\", ctrl, \" (mean)\")\nr_est, _, _ = parse_effect(t2[\"Reemployment\"][ATE])\nput_ri(\"ri_reemployment_ate_pp\", \"Reemployment\", ATE, r_est, \" (point estimate)\")\nu_est, u_lo, u_hi = parse_effect(t2[\"Weeks on UI\"][ATE])\nput_ri(\"ri_weeks_ui_ate\", \"Weeks on UI\", ATE, u_est, \" (point estimate)\")\nput_ri(\"ri_weeks_ui_ate_ci_low\", \"Weeks on UI\", ATE, u_lo, \" (95% CI lower bound)\")\nput_ri(\"ri_weeks_ui_ate_ci_high\", \"Weeks on UI\", ATE, u_hi, \" (95% CI upper bound)\")\n\n# Cross-table check values used only for data_issues\na19_cols, a19 = parse_reg_table(table_block(dcr, \"Table A19:\"))\na19_se = a19[\"ATT\"][\"CS-DR\"][1]\n\nderivation_notes = {\n    \"dcr_att_5km_ci\": (f\"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE using Table 6 printed \"\n                       f\"ATT {PRINTED['dcr_att_5km']} and SE {PRINTED['dcr_att_5km_se']} (clustered at MedStat region level); \"\n                       f\"bounds rounded to 6 decimals. Not printed in the source.\"),\n    \"ri_wage_ate_se\": (f\"SE = (upper - lower)/(2*1.96) from Table 2 printed 95% CI [{w_lo}, {w_hi}], assuming a symmetric \"\n                       f\"normal-approximation interval (the CI method is 'described in the main text', which is not in the extract); \"\n                       f\"rounded to 6 decimals. The source prints no standard error.\"),\n}\n\ndata_issues = [\n    {\"issue\": f\"{DCR_ID}: the 5 km CS-DR ATT -0.431 has SE {PRINTED['dcr_att_5km_se']} in Table 6 but {a19_se} in Table A19 (same estimator, buffer and window).\",\n     \"handling\": \"Keys name Table 6, so the Table 6 SE (0.210) is used for dcr_att_5km_se and the derived CI; discrepancy reported.\"},\n    {\"issue\": f\"{DCR_ID}: Table A19 note states 640 never-treated comparison regions whereas Tables 6/7 and the Figure 6 note state 608.\",\n     \"handling\": \"No requested key depends on it; reported only.\"},\n    {\"issue\": f\"{DCR_ID}: abstract says fatal drug-related hospitalisations fall by 35%, Section 6.1 says approximately 30% at 5 km; Table 7 5 km fatal ATT is -0.354 (exp(-0.354)-1 = -29.8%).\",\n     \"handling\": \"No requested key depends on the prose; table values used; reported only.\"},\n    {\"issue\": f\"{DCR_ID}: text calls HIV estimates 'insignificant and close to zero' while the Table 7 5 km HIV ATT is -0.340 (SE 0.294), similar in size to significant estimates.\",\n     \"handling\": \"Significance flag taken from the table stars (none); exact p-value not printed -> null.\"},\n    {\"issue\": f\"{DCR_ID}: Figure 7 note and Table 7 note describe four sub-categories while Table 7 shows five outcome rows (fatal has no ICD code listed).\",\n     \"handling\": \"Reported only.\"},\n    {\"issue\": f\"{RI_ID}: prose gives the weeks-on-UI CI as positive magnitudes [1.75 weeks, 2.24 weeks] while Table 2 prints [-2.24, -1.75].\",\n     \"handling\": \"Keys use Table 2 as printed (negative, lower bound first).\"},\n    {\"issue\": f\"{RI_ID}: rendering defect 'p ¡ 0.001' in the short-term results paragraph (intended 'p < 0.001').\",\n     \"handling\": \"No requested key affected; reported only.\"},\n    {\"issue\": f\"{RI_ID}: Tables 2 and 3 were re-set by the extract editor (one row per outcome); Table 3 column 1 duplicates Table 2's ATE column.\",\n     \"handling\": \"Values taken from Table 2 as the keys require; identical to Table 3 'Difference in means'.\"},\n]\n\nvalue_keys = [k for k in R]\nresults = dict(R)\nresults[\"locators\"] = {k: LOC[k] for k in R if k not in (\"dcr_hiv_att_5km_significant_10pct\", \"ri_wage_ate_se_reported\")}\nresults[\"printed_strings\"] = PRINTED\nresults[\"derivation_notes\"] = derivation_notes\nresults[\"task_id\"] = \"F4-conf-01\"\nresults[\"feasibility\"] = \"complete\"\nresults[\"feasibility_reason\"] = None\nresults[\"data_issues\"] = data_issues\nresults[\"n_input_rows\"] = {\"README.md\": wc_l(README_F), DCR_ID + \".txt\": wc_l(DCR_F), RI_ID + \".txt\": wc_l(RI_F)}\nresults[\"n_analysed\"] = len(value_keys)\nresults[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False)\n\nprint(\"Matched source lines:\")\nfor k, raw in MATCHED.items():\n    print(f\"  {k} = {R[k]!r}  <- {PRINTED[k]!r}  from: {' // '.join(r.strip() for r in raw)}\")\nfor k in (\"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\", \"dcr_att_5km_ci_low\",\n          \"dcr_att_5km_ci_high\", \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\"):\n    print(f\"  {k} = {R[k]!r}\")\nprint(f\"n_analysed = {len(value_keys)}; wrote out/results.json\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json | head -80"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1420,"cache_read_input_tokens":24761,"output_tokens":5527,"output_tokens_details":{"thinking_tokens":319},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1420},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs5GoA8S9Nps93wfLxA","requestRef":"17a03b69-5c29-45ab-9136-c95893a0c85b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"82df6f1d-9cd0-4853-a818-1aeed734e377","timestamp":"2026-10-30T05:25:11.954Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"82df6f1d-9cd0-4853-a818-1aeed734e377","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TuJJhCTUK5BvPfBxFQ8SFQ","type":"tool_result","content":"Matched source lines:\n  dcr_att_5km = -0.431  <- '-0.431**'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_5km_se = 0.21  <- '(0.210)'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_3km = -0.283  <- '-0.283'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_3km_se = 0.279  <- '(0.279)'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_obs_5km = 17250  <- '17,250'  from: Observations | 16,175 | 16,875 | 17,250\n  dcr_narcotics_att_3km = 0.549  <- '0.549***'  from: Narcotics | 1.186*** | 0.549*** | 0.390** // | (0.288) | (0.187) | (0.165)\n  dcr_narcotics_att_3km_se = 0.187  <- '(0.187)'  from: Narcotics | 1.186*** | 0.549*** | 0.390** // | (0.288) | (0.187) | (0.165)\n  dcr_fatal_att_1km = -1.207  <- '-1.207***'  from: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** // | (0.137) | (0.215) | (0.178)\n  dcr_fatal_att_1km_se = 0.137  <- '(0.137)'  from: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** // | (0.137) | (0.215) | (0.178)\n  dcr_hiv_att_5km = -0.34  <- '-0.340'  from: HIV | 0.063 | -0.239 | -0.340 // | (0.801) | (0.337) | (0.294)\n  dcr_hiv_att_5km_se = 0.294  <- '(0.294)'  from: HIV | 0.063 | -0.239 | -0.340 // | (0.801) | (0.337) | (0.294)\n  ri_wage_ate = 1153  <- '$1,153'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_wage_ate_ci_low = 166  <- '166'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_wage_ate_ci_high = 2141  <- '2,141'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_control_mean_wage = 33043  <- '$33,043 '  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_reemployment_ate_pp = 1.47  <- '1.47 pp'  from: Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n  ri_weeks_ui_ate = -1.99  <- '-1.99 weeks'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  ri_weeks_ui_ate_ci_low = -2.24  <- '-2.24'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  ri_weeks_ui_ate_ci_high = -1.75  <- '-1.75'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  dcr_hiv_att_5km_p_value = None\n  dcr_hiv_att_5km_significant_10pct = False\n  dcr_att_5km_ci_low = -0.8426\n  dcr_att_5km_ci_high = -0.0194\n  ri_wage_ate_se_reported = True\n  ri_wage_ate_se_derived = 503.826531\nn_analysed = 25; wrote out/results.json\n{\n  \"dcr_att_5km\": -0.431,\n  \"dcr_att_5km_se\": 0.21,\n  \"dcr_att_3km\": -0.283,\n  \"dcr_att_3km_se\": 0.279,\n  \"dcr_obs_5km\": 17250,\n  \"dcr_narcotics_att_3km\": 0.549,\n  \"dcr_narcotics_att_3km_se\": 0.187,\n  \"dcr_fatal_att_1km\": -1.207,\n  \"dcr_fatal_att_1km_se\": 0.137,\n  \"dcr_hiv_att_5km\": -0.34,\n  \"dcr_hiv_att_5km_se\": 0.294,\n  \"dcr_hiv_att_5km_p_value\": null,\n  \"dcr_hiv_att_5km_significant_10pct\": false,\n  \"dcr_att_5km_ci_low\": -0.8426,\n  \"dcr_att_5km_ci_high\": -0.0194,\n  \"ri_wage_ate\": 1153,\n  \"ri_wage_ate_ci_low\": 166,\n  \"ri_wage_ate_ci_high\": 2141,\n  \"ri_wage_ate_se_reported\": true,\n  \"ri_wage_ate_se_derived\": 503.826531,\n  \"ri_control_mean_wage\": 33043,\n  \"ri_reemployment_ate_pp\": 1.47,\n  \"ri_weeks_ui_ate\": -1.99,\n  \"ri_weeks_ui_ate_ci_low\": -2.24,\n  \"ri_weeks_ui_ate_ci_high\": -1.75,\n  \"locators\": {\n    \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n    \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n    \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n    \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n    \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n    \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n    \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the coefficient, SE and significance stars; 0 exact p-value strings found in the extract)\",\n    \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE\",\n    \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE\",\n    \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n    \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\",\n    \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI bounds\",\n    \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" (mean)\",\n    \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n    \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\"\n  },\n  \"printed_strings\": {\n    \"dcr_att_5km\": \"-0.431**\",\n    \"dcr_att_5km_se\": \"(0.210)\",\n    \"dcr_att_3km\": \"-0.283\",\n    \"dcr_att_3km_se\": \"(0.279)\",\n    \"dcr_obs_5km\": \"17,250\",\n    \"dcr_narcotics_att_3km\": \"0.549***\",\n    \"dcr_narcotics_att_3km_se\": \"(0.187)\",\n    \"dcr_fatal_att_1km\": \"-1.207***\",\n    \"dcr_fatal_att_1km_se\": \"(0.137)\",\n    \"dcr_hiv_att_5km\": \"-0.340\",\n    \"dcr_hiv_att_5km_se\": \"(0.294)\",\n    \"ri_wage_ate\": \"$1,153\",\n    \"ri_wage_ate_ci_low\": \"166\",\n    \"ri_wage_ate_ci_high\": \"2,141\",\n    \"ri_control_mean_wage\": \"$33,043 \",\n    \"ri_reemployment_ate_pp\": \"1.47 pp\",\n    \"ri_weeks_ui_ate\": \"-1.99 weeks\",\n    \"ri_weeks_ui_ate_ci_low\": \"-2.24\",\n    \"ri_weeks_ui_ate_ci_high\": \"-1.75\"\n  },\n  \"derivation_notes\": {\n    \"dcr_att_5km_ci\": \"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE using Table 6 printed ATT -0.431** and SE (0.210) (clustered at MedStat region level); bounds rounded to 6 decimals. Not printed in the source.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from Table 2 printed 95% CI [166, 2,141], assuming a symmetric normal-approximation interval (the CI method is 'described in the main text', which is not in the extract); rounded to 6 decimals. The source prints no standard error.\"\n  },\n  \"task_id\": \"F4-conf-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [","is_error":false}]},"uuid":"05a06a46-2d50-484e-a97e-bc70345eaf8d","timestamp":"2026-10-30T05:25:12.007Z","toolUseResult":{"stdout":"Matched source lines:\n  dcr_att_5km = -0.431  <- '-0.431**'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_5km_se = 0.21  <- '(0.210)'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_3km = -0.283  <- '-0.283'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_att_3km_se = 0.279  <- '(0.279)'  from: ATT | -0.399 | -0.283 | -0.431** // | (0.412) | (0.279) | (0.210)\n  dcr_obs_5km = 17250  <- '17,250'  from: Observations | 16,175 | 16,875 | 17,250\n  dcr_narcotics_att_3km = 0.549  <- '0.549***'  from: Narcotics | 1.186*** | 0.549*** | 0.390** // | (0.288) | (0.187) | (0.165)\n  dcr_narcotics_att_3km_se = 0.187  <- '(0.187)'  from: Narcotics | 1.186*** | 0.549*** | 0.390** // | (0.288) | (0.187) | (0.165)\n  dcr_fatal_att_1km = -1.207  <- '-1.207***'  from: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** // | (0.137) | (0.215) | (0.178)\n  dcr_fatal_att_1km_se = 0.137  <- '(0.137)'  from: Drug-related (fatal) | -1.207*** | -0.290 | -0.354** // | (0.137) | (0.215) | (0.178)\n  dcr_hiv_att_5km = -0.34  <- '-0.340'  from: HIV | 0.063 | -0.239 | -0.340 // | (0.801) | (0.337) | (0.294)\n  dcr_hiv_att_5km_se = 0.294  <- '(0.294)'  from: HIV | 0.063 | -0.239 | -0.340 // | (0.801) | (0.337) | (0.294)\n  ri_wage_ate = 1153  <- '$1,153'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_wage_ate_ci_low = 166  <- '166'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_wage_ate_ci_high = 2141  <- '2,141'  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_control_mean_wage = 33043  <- '$33,043 '  from: Wage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n  ri_reemployment_ate_pp = 1.47  <- '1.47 pp'  from: Reemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n  ri_weeks_ui_ate = -1.99  <- '-1.99 weeks'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  ri_weeks_ui_ate_ci_low = -2.24  <- '-2.24'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  ri_weeks_ui_ate_ci_high = -1.75  <- '-1.75'  from: Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n  dcr_hiv_att_5km_p_value = None\n  dcr_hiv_att_5km_significant_10pct = False\n  dcr_att_5km_ci_low = -0.8426\n  dcr_att_5km_ci_high = -0.0194\n  ri_wage_ate_se_reported = True\n  ri_wage_ate_se_derived = 503.826531\nn_analysed = 25; wrote out/results.json\n{\n  \"dcr_att_5km\": -0.431,\n  \"dcr_att_5km_se\": 0.21,\n  \"dcr_att_3km\": -0.283,\n  \"dcr_att_3km_se\": 0.279,\n  \"dcr_obs_5km\": 17250,\n  \"dcr_narcotics_att_3km\": 0.549,\n  \"dcr_narcotics_att_3km_se\": 0.187,\n  \"dcr_fatal_att_1km\": -1.207,\n  \"dcr_fatal_att_1km_se\": 0.137,\n  \"dcr_hiv_att_5km\": -0.34,\n  \"dcr_hiv_att_5km_se\": 0.294,\n  \"dcr_hiv_att_5km_p_value\": null,\n  \"dcr_hiv_att_5km_significant_10pct\": false,\n  \"dcr_att_5km_ci_low\": -0.8426,\n  \"dcr_att_5km_ci_high\": -0.0194,\n  \"ri_wage_ate\": 1153,\n  \"ri_wage_ate_ci_low\": 166,\n  \"ri_wage_ate_ci_high\": 2141,\n  \"ri_wage_ate_se_reported\": true,\n  \"ri_wage_ate_se_derived\": 503.826531,\n  \"ri_control_mean_wage\": 33043,\n  \"ri_reemployment_ate_pp\": 1.47,\n  \"ri_weeks_ui_ate\": -1.99,\n  \"ri_weeks_ui_ate_ci_low\": -2.24,\n  \"ri_weeks_ui_ate_ci_high\": -1.75,\n  \"locators\": {\n    \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n    \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n    \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n    \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n    \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n    \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n    \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" (standard error in parentheses on the line below)\",\n    \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the coefficient, SE and significance stars; 0 exact p-value strings found in the extract)\",\n    \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE\",\n    \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | derived from printed ATT and SE\",\n    \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n    \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\",\n    \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived from printed 95% CI bounds\",\n    \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" (mean)\",\n    \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (point estimate)\",\n    \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI lower bound)\",\n    \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" (95% CI upper bound)\"\n  },\n  \"printed_strings\": {\n    \"dcr_att_5km\": \"-0.431**\",\n    \"dcr_att_5km_se\": \"(0.210)\",\n    \"dcr_att_3km\": \"-0.283\",\n    \"dcr_att_3km_se\": \"(0.279)\",\n    \"dcr_obs_5km\": \"17,250\",\n    \"dcr_narcotics_att_3km\": \"0.549***\",\n    \"dcr_narcotics_att_3km_se\": \"(0.187)\",\n    \"dcr_fatal_att_1km\": \"-1.207***\",\n    \"dcr_fatal_att_1km_se\": \"(0.137)\",\n    \"dcr_hiv_att_5km\": \"-0.340\",\n    \"dcr_hiv_att_5km_se\": \"(0.294)\",\n    \"ri_wage_ate\": \"$1,153\",\n    \"ri_wage_ate_ci_low\": \"166\",\n    \"ri_wage_ate_ci_high\": \"2,141\",\n    \"ri_control_mean_wage\": \"$33,043 \",\n    \"ri_reemployment_ate_pp\": \"1.47 pp\",\n    \"ri_weeks_ui_ate\": \"-1.99 weeks\",\n    \"ri_weeks_ui_ate_ci_low\": \"-2.24\",\n    \"ri_weeks_ui_ate_ci_high\": \"-1.75\"\n  },\n  \"derivation_notes\": {\n    \"dcr_att_5km_ci\": \"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE using Table 6 printed ATT -0.431** and SE (0.210) (clustered at MedStat region level); bounds rounded to 6 decimals. Not printed in the source.\",\n    \"ri_wage_ate_se\": \"SE = (upper - lower)/(2*1.96) from Table 2 printed 95% CI [166, 2,141], assuming a symmetric normal-approximation interval (the CI method is 'described in the main text', which is not in the extract); rounded to 6 decimals. The source prints no standard error.\"\n  },\n  \"task_id\": \"F4-conf-01\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"82df6f1d-9cd0-4853-a818-1aeed734e377","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"cafb0cb247e9291af58f7367a3fa232a06e80997fd19ed1c1d64c231c3c921d7","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01TuJJhCTUK5BvPfBxFQ8SFQ","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed estimates from two arXiv extracts and derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nDCR_F = os.path.join(ROOT, \"data\", \"sources\", DCR_ID + \".txt\")\nRI_F = os.path.join(ROOT, \"data\", \"sources\", RI_ID + \".txt\")\nREADME_F = os.path.join(ROOT, \"data\", \"README.md\")\nZ = 1.96  # normal critical value for two-sided 95%\n\ndef read(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read()\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\ndef num(s):\n    \"\"\"Parse a printed number string (keep sign; strip $, commas, stars, %, units).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\")\n    m = re.search(r\"-?\\d+(?:\\.\\d+)?\", s)\n    return m.group(0)\n\ndef to_val(s):\n    return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef table_block(text, title_prefix):\n    lines = text.splitlines()\n    start = next(i for i, l in enumerate(lines) if l.startswith(title_prefix))\n    block = []\n    for l in lines[start + 1:]:\n        if l.startswith(\"Note:\"):\n            break\n        if l.strip():\n            block.append(l)\n    return block\n\ndef parse_reg_table(block):\n    \"\"\"Return header cols, {row_label: {col: (coef_str, se_str or None, raw lines)}}.\"\"\"\n    header = [c.strip() for c in block[0].split(\"|\")]\n    cols = [c for c in header if c and c != \"Outcome\"]\n    rows, last = {}, None\n    for l in block[1:]:\n        cells = [c.strip() for c in l.split(\"|\")]\n        if cells[0] == \"\" and last is not None and all(c.startswith(\"(\") for c in cells[1:] if c):\n            for c, v in zip(cols, cells[1:]):\n                coef, _, raw = rows[last][c]\n                rows[last][c] = (coef, v, raw + [l])\n            continue\n        label = cells[0]\n        rows[label] = {c: (v, None, [l]) for c, v in zip(cols, cells[1:])}\n        last = label\n    return cols, rows\n\ndcr = read(DCR_F); ri = read(RI_F)\nt6_cols, t6 = parse_reg_table(table_block(dcr, \"Table 6:\"))\nt7_cols, t7 = parse_reg_table(table_block(dcr, \"Table 7:\"))\n\nR, LOC, PRINTED, MATCHED = {}, {}, {}, {}\n\ndef loc(src, tab, row, col, extra=\"\"):\n    return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\ndef put_dcr(key, tab, rows, row, col, what):\n    coef, se, raw = rows[row][col]\n    s = coef if what == \"coef\" else se\n    p = num(s)\n    R[key] = to_val(p); PRINTED[key] = s\n    LOC[key] = loc(DCR_ID, tab, row, col, \"\" if what == \"coef\" else \" (standard error in parentheses on the line below)\")\n    MATCHED[key] = raw\n    return coef\n\nput_dcr(\"dcr_att_5km\", 6, t6, \"ATT\", \"5 km\", \"coef\")\nput_dcr(\"dcr_att_5km_se\", 6, t6, \"ATT\", \"5 km\", \"se\")\nput_dcr(\"dcr_att_3km\", 6, t6, \"ATT\", \"3 km\", \"coef\")\nput_dcr(\"dcr_att_3km_se\", 6, t6, \"ATT\", \"3 km\", \"se\")\nput_dcr(\"dcr_obs_5km\", 6, t6, \"Observations\", \"5 km\", \"coef\")\nput_dcr(\"dcr_narcotics_att_3km\", 7, t7, \"Narcotics\", \"3 km\", \"coef\")\nput_dcr(\"dcr_narcotics_att_3km_se\", 7, t7, \"Narcotics\", \"3 km\", \"se\")\nput_dcr(\"dcr_fatal_att_1km\", 7, t7, \"Drug-related (fatal)\", \"1 km\", \"coef\")\nput_dcr(\"dcr_fatal_att_1km_se\", 7, t7, \"Drug-related (fatal)\", \"1 km\", \"se\")\nhiv_coef = put_dcr(\"dcr_hiv_att_5km\", 7, t7, \"HIV\", \"5 km\", \"coef\")\nput_dcr(\"dcr_hiv_att_5km_se\", 7, t7, \"HIV\", \"5 km\", \"se\")\n\n# HIV exact p-value: search the extract for any printed p-value (p= / p-value) -- none expected\npv_hits = [l for l in dcr.splitlines() if re.search(r\"p\\s*=\\s*0?\\.\\d|p-value\", l, re.I)]\nR[\"dcr_hiv_att_5km_p_value\"] = None\nLOC[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE and \"\n                                  f\"significance stars; {len(pv_hits)} exact p-value strings found in the extract)\")\nstars = len(hiv_coef) - len(hiv_coef.rstrip(\"*\"))\nR[\"dcr_hiv_att_5km_significant_10pct\"] = stars >= 1  # * p<0.10 or stronger\n\n# Derived 95% CI from Table 6 printed values\nb, se = R[\"dcr_att_5km\"], R[\"dcr_att_5km_se\"]\nR[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 6)\nR[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 6)\nfor k in (\"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\"):\n    LOC[k] = (f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE')\n\n# RESEA Table 2\nlines = ri.splitlines()\nhdr_i = next(i for i, l in enumerate(lines) if l.startswith(\"Outcome | Treatment | Control\"))\nt2_cols = [c.strip() for c in lines[hdr_i].split(\"|\")]\nt2 = {}\nfor l in lines[hdr_i + 1:]:\n    if not l.strip():\n        break\n    cells = [c.strip() for c in l.split(\"|\")]\n    t2[cells[0]] = dict(zip(t2_cols[1:], cells[1:])) | {\"_raw\": l}\nATE = \"Average treatment effect estimate\"\n\ndef parse_effect(cell):\n    est = cell.split(\";\")[0]\n    m = re.search(r\"95% CI:\\s*\\[([^\\]]+)\\]\", cell)\n    lo, hi = [x.strip() for x in m.group(1).split(\", \")]\n    return est, lo, hi\n\ndef put_ri(key, row, col, s, part):\n    R[key] = to_val(num(s)); PRINTED[key] = s\n    LOC[key] = loc(RI_ID, 2, row, col, part); MATCHED[key] = [t2[row][\"_raw\"]]\n\nw_est, w_lo, w_hi = parse_effect(t2[\"Wage\"][ATE])\nput_ri(\"ri_wage_ate\", \"Wage\", ATE, w_est, \" (point estimate)\")\nput_ri(\"ri_wage_ate_ci_low\", \"Wage\", ATE, w_lo, \" (95% CI lower bound)\")\nput_ri(\"ri_wage_ate_ci_high\", \"Wage\", ATE, w_hi, \" (95% CI upper bound)\")\nR[\"ri_wage_ate_se_reported\"] = (\"SE\" in t2[\"Wage\"][ATE].replace(\"SD\", \"\")) or (\"standard error\" in ri.lower())\nR[\"ri_wage_ate_se_derived\"] = round((R[\"ri_wage_ate_ci_high\"] - R[\"ri_wage_ate_ci_low\"]) / (2 * Z), 6)\nLOC[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived from printed 95% CI bounds')\nctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0]\nput_ri(\"ri_control_mean_wage\", \"Wage\", \"Control\", ctrl, \" (mean)\")\nr_est, _, _ = parse_effect(t2[\"Reemployment\"][ATE])\nput_ri(\"ri_reemployment_ate_pp\", \"Reemployment\", ATE, r_est, \" (point estimate)\")\nu_est, u_lo, u_hi = parse_effect(t2[\"Weeks on UI\"][ATE])\nput_ri(\"ri_weeks_ui_ate\", \"Weeks on UI\", ATE, u_est, \" (point estimate)\")\nput_ri(\"ri_weeks_ui_ate_ci_low\", \"Weeks on UI\", ATE, u_lo, \" (95% CI lower bound)\")\nput_ri(\"ri_weeks_ui_ate_ci_high\", \"Weeks on UI\", ATE, u_hi, \" (95% CI upper bound)\")\n\n# Cross-table check values used only for data_issues\na19_cols, a19 = parse_reg_table(table_block(dcr, \"Table A19:\"))\na19_se = a19[\"ATT\"][\"CS-DR\"][1]\n\nderivation_notes = {\n    \"dcr_att_5km_ci\": (f\"Normal approximation, critical value 1.96: CI = ATT +/- 1.96*SE using Table 6 printed \"\n                       f\"ATT {PRINTED['dcr_att_5km']} and SE {PRINTED['dcr_att_5km_se']} (clustered at MedStat region level); \"\n                       f\"bounds rounded to 6 decimals. Not printed in the source.\"),\n    \"ri_wage_ate_se\": (f\"SE = (upper - lower)/(2*1.96) from Table 2 printed 95% CI [{w_lo}, {w_hi}], assuming a symmetric \"\n                       f\"normal-approximation interval (the CI method is 'described in the main text', which is not in the extract); \"\n                       f\"rounded to 6 decimals. The source prints no standard error.\"),\n}\n\ndata_issues = [\n    {\"issue\": f\"{DCR_ID}: the 5 km CS-DR ATT -0.431 has SE {PRINTED['dcr_att_5km_se']} in Table 6 but {a19_se} in Table A19 (same estimator, buffer and window).\",\n     \"handling\": \"Keys name Table 6, so the Table 6 SE (0.210) is used for dcr_att_5km_se and the derived CI; discrepancy reported.\"},\n    {\"issue\": f\"{DCR_ID}: Table A19 note states 640 never-treated comparison regions whereas Tables 6/7 and the Figure 6 note state 608.\",\n     \"handling\": \"No requested key depends on it; reported only.\"},\n    {\"issue\": f\"{DCR_ID}: abstract says fatal drug-related hospitalisations fall by 35%, Section 6.1 says approximately 30% at 5 km; Table 7 5 km fatal ATT is -0.354 (exp(-0.354)-1 = -29.8%).\",\n     \"handling\": \"No requested key depends on the prose; table values used; reported only.\"},\n    {\"issue\": f\"{DCR_ID}: text calls HIV estimates 'insignificant and close to zero' while the Table 7 5 km HIV ATT is -0.340 (SE 0.294), similar in size to significant estimates.\",\n     \"handling\": \"Significance flag taken from the table stars (none); exact p-value not printed -> null.\"},\n    {\"issue\": f\"{DCR_ID}: Figure 7 note and Table 7 note describe four sub-categories while Table 7 shows five outcome rows (fatal has no ICD code listed).\",\n     \"handling\": \"Reported only.\"},\n    {\"issue\": f\"{RI_ID}: prose gives the weeks-on-UI CI as positive magnitudes [1.75 weeks, 2.24 weeks] while Table 2 prints [-2.24, -1.75].\",\n     \"handling\": \"Keys use Table 2 as printed (negative, lower bound first).\"},\n    {\"issue\": f\"{RI_ID}: rendering defect 'p ¡ 0.001' in the short-term results paragraph (intended 'p < 0.001').\",\n     \"handling\": \"No requested key affected; reported only.\"},\n    {\"issue\": f\"{RI_ID}: Tables 2 and 3 were re-set by the extract editor (one row per outcome); Table 3 column 1 duplicates Table 2's ATE column.\",\n     \"handling\": \"Values taken from Table 2 as the keys require; identical to Table 3 'Difference in means'.\"},\n]\n\nvalue_keys = [k for k in R]\nresults = dict(R)\nresults[\"locators\"] = {k: LOC[k] for k in R if k not in (\"dcr_hiv_att_5km_significant_10pct\", \"ri_wage_ate_se_reported\")}\nresults[\"printed_strings\"] = PRINTED\nresults[\"derivation_notes\"] = derivation_notes\nresults[\"task_id\"] = \"F4-conf-01\"\nresults[\"feasibility\"] = \"complete\"\nresults[\"feasibility_reason\"] = None\nresults[\"data_issues\"] = data_issues\nresults[\"n_input_rows\"] = {\"README.md\": wc_l(README_F), DCR_ID + \".txt\": wc_l(DCR_F), RI_ID + \".txt\": wc_l(RI_F)}\nresults[\"n_analysed\"] = len(value_keys)\nresults[\"random_seed\"] = None\n\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False)\n\nprint(\"Matched source lines:\")\nfor k, raw in MATCHED.items():\n    print(f\"  {k} = {R[k]!r}  <- {PRINTED[k]!r}  from: {' // '.join(r.strip() for r in raw)}\")\nfor k in (\"dcr_hiv_att_5km_p_value\", \"dcr_hiv_att_5km_significant_10pct\", \"dcr_att_5km_ci_low\",\n          \"dcr_att_5km_ci_high\", \"ri_wage_ate_se_reported\", \"ri_wage_ate_se_derived\"):\n    print(f\"  {k} = {R[k]!r}\")\nprint(f\"n_analysed = {len(value_keys)}; wrote out/results.json\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json | head 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CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n10:Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n19:Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n35:Table 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n39:The results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n41:As noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n45:We find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n56:Table 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.","is_error":false}]},"uuid":"d537b3de-758e-44c6-a28c-6764c9347f40","timestamp":"2026-10-30T05:25:17.283Z","toolUseResult":{"stdout":"6:License: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n10:Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n19:Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n35:Table 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n39:The results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n41:As noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n45:We find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n56:Table 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). 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I'll restrict the check to the paper body (after the header separator) and to the wage row."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3147,"cache_read_input_tokens":36414,"output_tokens":608,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3147},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs9PPPoUbQmWkiqE3xp","requestRef":"e177b6d5-7547-4fc0-94d7-3f1eff5fb9cd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"94fe1f50-c857-4a88-9c9f-3bbbc9b0519e","timestamp":"2026-10-30T05:25:19.265Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"94fe1f50-c857-4a88-9c9f-3bbbc9b0519e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs9Pa4aebBhcpQ239rS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fifkhqw3U6fc235SAixBQ3","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''R[\"ri_wage_ate_se_reported\"] = (\"SE\" in t2[\"Wage\"][ATE].replace(\"SD\", \"\")) or (\"standard error\" in ri.lower())''',\n'''# Search the wage row and the paper body (after the editor's header) for a printed standard error.\nri_body = ri.split(\"=\" * 20, 1)[1]\nwage_row = t2[\"Wage\"][\"_raw\"]\nR[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\\\bSE\\\\b|s\\\\.e\\\\.|standard error\", wage_row, re.I)\n                                    or re.search(r\"standard error|\\\\bSE\\\\b\", ri_body, re.I))''')\ns=s.replace('ctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0]','ctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0].strip()')\nopen(p,'w').write(s)\nEOF\nsed -i '' 's/,26,26,0,All 26 requested/,25,25,0,All 25 requested/' out/exclusions.csv\n~/wp29/venv/bin/python out/analysis.py | tail -4; grep -n \"se_reported\\|n_analysed\\|n_input\" -A0 out/results.json; sed -n '/n_input_rows/,/}/p' out/results.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3147,"cache_read_input_tokens":36414,"output_tokens":608,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":3147},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs9PPPoUbQmWkiqE3xp","requestRef":"e177b6d5-7547-4fc0-94d7-3f1eff5fb9cd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7fc3a351-11fa-42b8-bef9-acad7eb89675","timestamp":"2026-10-30T05:25:23.342Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"7fc3a351-11fa-42b8-bef9-acad7eb89675","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Fifkhqw3U6fc235SAixBQ3","type":"tool_result","content":"  dcr_att_5km_ci_high = -0.0194\n  ri_wage_ate_se_reported = False\n  ri_wage_ate_se_derived = 503.826531\nn_analysed = 25; wrote out/results.json\n20:  \"ri_wage_ate_se_reported\": false,\n114:  \"n_input_rows\": {\n119:  \"n_analysed\": 25,\n  \"n_input_rows\": {\n    \"README.md\": 10,\n    \"arxiv-2609.27732v1.txt\": 134,\n    \"arxiv-2606.14621v1.txt\": 58\n  },","is_error":false}]},"uuid":"e53d4e6f-4dde-4b29-93c6-43b35a74b61c","timestamp":"2026-10-30T05:25:23.433Z","toolUseResult":{"stdout":"  dcr_att_5km_ci_high = -0.0194\n  ri_wage_ate_se_reported = False\n  ri_wage_ate_se_derived = 503.826531\nn_analysed = 25; wrote out/results.json\n20:  \"ri_wage_ate_se_reported\": false,\n114:  \"n_input_rows\": {\n119:  \"n_analysed\": 25,\n  \"n_input_rows\": {\n    \"README.md\": 10,\n    \"arxiv-2609.27732v1.txt\": 134,\n    \"arxiv-2606.14621v1.txt\": 58\n  },","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7fc3a351-11fa-42b8-bef9-acad7eb89675","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}64{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"a4d04eac129ed668cccfac55d08cf95cb4d32f7f602937cf595f7f0d6ade7ee7","message":{"role":"assistant","content":[{"type":"text","text":"The match came from the editor's generic rendering note. I'll restrict the check to the paper body (after the header separator) and to the wage row."},{"type":"tool_use","id":"toolu_01Fifkhqw3U6fc235SAixBQ3","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''R[\"ri_wage_ate_se_reported\"] = (\"SE\" in t2[\"Wage\"][ATE].replace(\"SD\", \"\")) or (\"standard error\" in ri.lower())''',\n'''# Search the wage row and the paper body (after the editor's header) for a printed standard error.\nri_body = ri.split(\"=\" * 20, 1)[1]\nwage_row = t2[\"Wage\"][\"_raw\"]\nR[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\\\bSE\\\\b|s\\\\.e\\\\.|standard error\", wage_row, re.I)\n                                    or re.search(r\"standard error|\\\\bSE\\\\b\", ri_body, re.I))''')\ns=s.replace('ctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0]','ctrl = t2[\"Wage\"][\"Control\"].split(\"(\")[0].strip()')\nopen(p,'w').write(s)\nEOF\nsed -i '' 's/,26,26,0,All 26 requested/,25,25,0,All 25 requested/' out/exclusions.csv\n~/wp29/venv/bin/python out/analysis.py | tail -4; grep -n \"se_reported\\|n_analysed\\|n_input\" -A0 out/results.json; sed -n '/n_input_rows/,/}/p' out/results.json"}}]}}65{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"fa94a9b2-8c19-4ce9-ac47-4029765c6f06","timestamp":"2026-10-30T05:25:23.436Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"e177b6d5-7547-4fc0-94d7-3f1eff5fb9cd","keep":18,"tail":["a4d04eac129ed668cccfac55d08cf95cb4d32f7f602937cf595f7f0d6ade7ee7","42967beb6b7c0b78dabf00d3905f5a1d053972553eec8cdcd36f99f695b20db8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"be53bc5c624d53b1b6fa34095cf9663956c0ecfa7c085a5ad826d49b289f56fe"}66{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e53d4e6f-4dde-4b29-93c6-43b35a74b61c","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}67{"type":"atis-latch","atis":"","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}68{"parentUuid":"e53d4e6f-4dde-4b29-93c6-43b35a74b61c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs9qxKKDKVex5ymMwQj","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSogcKEAgTGAI4AUIIdGhpbmtpbmcSDBytBnI5/P7K4qWlWhoMQ5240i9ZDUG+wSurIjC0kVJFyc71MnbCNxEHjGhdqq0D97JihVD04GOui2sH3riLazKKvn2RTKnwhDVoktwqvwbPJQByJKiwHErD2GtYaM+X4l9ZH/haoigkJ2x77yURH0TWgtw875VKsMW/1DEyyqVialsdFxdhCAqiQBj44Jd1tD2WYabDrKmHnSmYQwRPsiBDCcSQ8ZeJLYsX+ueicTo9r7A+ai8eT3bv5yA7aoICYHOmzu/25Ah2WyuVN/7++aiXrzaIJHUtu/PPyr8wINB7wsZ7KhAwNjuF4eBt8sPmkB4im0fL2XUenaDKtIa8bVD/pPFPtZl4S4Q10MinHyVCInOSiSth5ZQZdC9wq0Ti/2Vc1s6owZArGAIi/RUTH9pBQGzfat0EPpM6qsJLkTh3Z+gxY/0Ybep5QzC1KUs76CvwAAV6cQgyFn2M4bDGWNE/BtkV4rrNWLLCfE1zL1xKe9f7fXSvIqAjeJbShwjN1YUCdzg/RuqV8EsNzpXkVdlNNqEesgdbNYsuz5TNkbizEcbwjluMenjkYg7C/A9BnenyVaTFD9Cvy+L99w0eqihAEiZQrB8816YhfMYN/d4S4ZmjPrHxk2rgRC0dKEJzuMraxHXPtkdvWz4nqpKm3WjYFHcAuNAp1j2n/7xBHHwLmQoY9PmpnW8HrCm3230W6wMRAQBELngK18NnuCOWbEMPCZYogUtBbJrBkIqUkyHQhZf60jFU0aeJPjdMAqn8pj+ERbHXVp1NgFdBv6Q427du2y7D5HauTNCErtCSpvxBOVLqgCkMPW8kf64YUzMry6iPw62uQoHTKnuooN9Qh5TeUlayakqQdDpBBbR1/BvH/KD1NhkWnXEzhvOMsnOvd7K52S4BgG7/Lhvpt8R7jqVo/8HGs/VbY6UIFMsqm5QxBnGB7ld9c3D2lKSTbaN4fKyR+U+otzSNrEkemejCq20jb5nt4fOmz5T62/LRmCjnSupSWyuKRysJAnZcTHNyp26km9aarXr9Yp4chkQcUB0G31ipVmKvaQwE60YKnW2z4O+puynwmKYjaba2f4fZro6p+wukP3f+9x5gP4GKW2k7v+CZZqv7ubZwzkOeFR4UskH3n51b5mRZOJJXwx/DS4T21CCO9qypl0Tqj2OuizwAyveXKhKDZtGNGzkoWBIwqOA30T1Eu5VpE7KPKgEYAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":774,"cache_read_input_tokens":39561,"output_tokens":1469,"output_tokens_details":{"thinking_tokens":57},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":774},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":614,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs9qgTEcco6km56rnnM","requestRef":"fa94a9b2-8c19-4ce9-ac47-4029765c6f06","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d58b5566-fd8c-4038-9af1-1f4a37eb8603","timestamp":"2026-10-30T05:25:27.072Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"d58b5566-fd8c-4038-9af1-1f4a37eb8603","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs9qxKKDKVex5ymMwQj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FKYFkCYu4wHBSoUt8cCjMu","name":"Bash","input":{"command":"sed -i '' 's/26 requested/25 requested/' out/state/data_audit.md\ncp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json; r=json.load(open('out/results.json'))\nc=[]\nc.append((\"CI contains estimate\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nc.append((\"CI half-width = 1.96*SE\", abs((r['dcr_att_5km_ci_high']-r['dcr_att_5km_ci_low'])/2-1.96*r['dcr_att_5km_se'])<1e-9))\nc.append((\"derived 95% CI excludes 0, consistent with ** (p<0.05)\", r['dcr_att_5km_ci_high']<0))\nc.append((\"wage CI contains ATE\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nc.append((\"wage CI near-symmetric (|asym|<=2)\", abs((r['ri_wage_ate']-r['ri_wage_ate_ci_low'])-(r['ri_wage_ate_ci_high']-r['ri_wage_ate']))<=2))\nc.append((\"derived SE positive\", r['ri_wage_ate_se_derived']>0))\nfrom scipy.stats import norm\nz=r['ri_wage_ate']/r['ri_wage_ate_se_derived']; p=2*norm.sf(z)\nc.append((f\"implied p from derived SE ({p:.4f}) ~ printed p=0.022\", abs(p-0.022)<0.003))\nc.append((\"weeks CI ordered and contains ATE\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nc.append((\"all SEs positive\", all(r[k]>0 for k in r if k.endswith('_se'))))\nc.append((\"HIV: |t|=%.2f <1.645 consistent with no star\"%(abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])), abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])<1.645 and not r['dcr_hiv_att_5km_significant_10pct']))\nfor k in ['dcr_narcotics_att_3km','dcr_fatal_att_1km']:\n    t=abs(r[k]/r[k+'_se']); c.append((f\"{k} |t|={t:.2f}>2.576 consistent with ***\", t>2.576))\nt=abs(r['dcr_att_5km']/r['dcr_att_5km_se']); c.append((f\"dcr_att_5km |t|={t:.2f} in (1.96,2.576) consistent with **\",1.96<t<2.576))\nc.append((\"p-value null with 'not reported' locator\", r['dcr_hiv_att_5km_p_value'] is None and 'not reported' in r['locators']['dcr_hiv_att_5km_p_value']))\nc.append((\"locators cover all keys except 2 booleans\", len(r['locators'])==r['n_analysed']-2))\nc.append((\"exclusion ledger: 25-0=25=n_analysed\", r['n_analysed']==25))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of out/analysis.py reproduced out/results.json byte-identically (cmp): PASS\\n\")\n    for n,ok in c: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nprint(open('out/state/checks.md').read())\nEOF"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":774,"cache_read_input_tokens":39561,"output_tokens":1469,"output_tokens_details":{"thinking_tokens":57},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":774},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs9qgTEcco6km56rnnM","requestRef":"fa94a9b2-8c19-4ce9-ac47-4029765c6f06","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a23eecbb-b7dc-40fc-a555-7c168060e189","timestamp":"2026-10-30T05:25:37.015Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"a23eecbb-b7dc-40fc-a555-7c168060e189","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FKYFkCYu4wHBSoUt8cCjMu","type":"tool_result","content":"IDENTICAL\n# Verification checks (P4)\n\n- Clean re-run of out/analysis.py reproduced out/results.json byte-identically (cmp): PASS\n- CI contains estimate: PASS\n- CI half-width = 1.96*SE: PASS\n- derived 95% CI excludes 0, consistent with ** (p<0.05): PASS\n- wage CI contains ATE: PASS\n- wage CI near-symmetric (|asym|<=2): PASS\n- derived SE positive: PASS\n- implied p from derived SE (0.0221) ~ printed p=0.022: PASS\n- weeks CI ordered and contains ATE: PASS\n- all SEs positive: PASS\n- HIV: |t|=1.16 <1.645 consistent with no star: PASS\n- dcr_narcotics_att_3km |t|=2.94>2.576 consistent with ***: PASS\n- dcr_fatal_att_1km |t|=8.81>2.576 consistent with ***: PASS\n- dcr_att_5km |t|=2.05 in (1.96,2.576) consistent with **: PASS\n- p-value null with 'not reported' locator: PASS\n- locators cover all keys except 2 booleans: PASS\n- exclusion ledger: 25-0=25=n_analysed: PASS","is_error":false}]},"uuid":"e5262139-7bad-4afd-9518-18506082e2bb","timestamp":"2026-10-30T05:25:37.506Z","toolUseResult":{"stdout":"IDENTICAL\n# Verification checks (P4)\n\n- Clean re-run of out/analysis.py reproduced out/results.json byte-identically (cmp): PASS\n- CI contains estimate: PASS\n- CI half-width = 1.96*SE: PASS\n- derived 95% CI excludes 0, consistent with ** (p<0.05): PASS\n- wage CI contains ATE: PASS\n- wage CI near-symmetric (|asym|<=2): PASS\n- derived SE positive: PASS\n- implied p from derived SE (0.0221) ~ printed p=0.022: PASS\n- weeks CI ordered and contains ATE: PASS\n- all SEs positive: PASS\n- HIV: |t|=1.16 <1.645 consistent with no star: PASS\n- dcr_narcotics_att_3km |t|=2.94>2.576 consistent with ***: PASS\n- dcr_fatal_att_1km |t|=8.81>2.576 consistent with ***: PASS\n- dcr_att_5km |t|=2.05 in (1.96,2.576) consistent with **: PASS\n- p-value null with 'not reported' locator: PASS\n- locators cover all keys except 2 booleans: PASS\n- exclusion ledger: 25-0=25=n_analysed: PASS","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a23eecbb-b7dc-40fc-a555-7c168060e189","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"a3216ca59e0ee7df8ccc013aa053e74c16d8e2d0dbad0c256c38e816004a758e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01FKYFkCYu4wHBSoUt8cCjMu","name":"Bash","input":{"command":"sed -i '' 's/26 requested/25 requested/' out/state/data_audit.md\ncp out/results.json /tmp/r1.json && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json; r=json.load(open('out/results.json'))\nc=[]\nc.append((\"CI contains estimate\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nc.append((\"CI half-width = 1.96*SE\", abs((r['dcr_att_5km_ci_high']-r['dcr_att_5km_ci_low'])/2-1.96*r['dcr_att_5km_se'])<1e-9))\nc.append((\"derived 95% CI excludes 0, consistent with ** (p<0.05)\", r['dcr_att_5km_ci_high']<0))\nc.append((\"wage CI contains ATE\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nc.append((\"wage CI near-symmetric (|asym|<=2)\", abs((r['ri_wage_ate']-r['ri_wage_ate_ci_low'])-(r['ri_wage_ate_ci_high']-r['ri_wage_ate']))<=2))\nc.append((\"derived SE positive\", r['ri_wage_ate_se_derived']>0))\nfrom scipy.stats import norm\nz=r['ri_wage_ate']/r['ri_wage_ate_se_derived']; p=2*norm.sf(z)\nc.append((f\"implied p from derived SE ({p:.4f}) ~ printed p=0.022\", abs(p-0.022)<0.003))\nc.append((\"weeks CI ordered and contains ATE\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nc.append((\"all SEs positive\", all(r[k]>0 for k in r if k.endswith('_se'))))\nc.append((\"HIV: |t|=%.2f <1.645 consistent with no star\"%(abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])), abs(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])<1.645 and not r['dcr_hiv_att_5km_significant_10pct']))\nfor k in ['dcr_narcotics_att_3km','dcr_fatal_att_1km']:\n    t=abs(r[k]/r[k+'_se']); c.append((f\"{k} |t|={t:.2f}>2.576 consistent with ***\", t>2.576))\nt=abs(r['dcr_att_5km']/r['dcr_att_5km_se']); c.append((f\"dcr_att_5km |t|={t:.2f} in (1.96,2.576) consistent with **\",1.96<t<2.576))\nc.append((\"p-value null with 'not reported' locator\", r['dcr_hiv_att_5km_p_value'] is None and 'not reported' in r['locators']['dcr_hiv_att_5km_p_value']))\nc.append((\"locators cover all keys except 2 booleans\", len(r['locators'])==r['n_analysed']-2))\nc.append((\"exclusion ledger: 25-0=25=n_analysed\", r['n_analysed']==25))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of out/analysis.py reproduced out/results.json byte-identically (cmp): PASS\\n\")\n    for n,ok in c: f.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nprint(open('out/state/checks.md').read())\nEOF"}}]}}72{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"e1e8e727-0f98-4c9f-838e-068505e25526","timestamp":"2026-10-30T05:25:37.509Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"fa94a9b2-8c19-4ce9-ac47-4029765c6f06","keep":20,"tail":["a3216ca59e0ee7df8ccc013aa053e74c16d8e2d0dbad0c256c38e816004a758e","54709f5a11af4b36d9989f661f3474f816cb3fb45a2d2f8404a0f47f85be6fdc"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"09b65c230b05ad6eb1bef396a90ce9f72541d2ebb0b053d87f2124c74d692e97"}73{"parentUuid":"e5262139-7bad-4afd-9518-18506082e2bb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAtD2WgHsPwQTbNQMd","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1910,"cache_read_input_tokens":40335,"output_tokens":679,"output_tokens_details":{"thinking_tokens":121},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1910},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1154,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsAssh2aJxDme89vrp4","requestRef":"e1e8e727-0f98-4c9f-838e-068505e25526","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3de0662b-2f68-4df5-98c1-a99d5c4dbf95","timestamp":"2026-10-30T05:25:40.564Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"3de0662b-2f68-4df5-98c1-a99d5c4dbf95","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAtD2WgHsPwQTbNQMd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LqWnzYUmbZqPpemfx8Zu4Q","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 data files match README/bundle\",\"line counts recorded (10/134/58)\",\"9 anomalies documented in data_audit.md\",\"exclusions.csv: 25 items, 0 removed\"],\"notes\":\"Table 6 SE (0.210) preferred over Table A19 SE (0.218) per key definition\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"Baseline = direct verbatim extraction of printed values from the named tables (the estimands are the printed numbers; no simpler estimate exists); values recorded under the required keys\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"out/analysis.py parses data/ only, prints matched source lines, no randomness (random_seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"17 consistency checks PASS (see checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed: extraction script written; fixed se_reported false-positive (matched editor header).\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1910,"cache_read_input_tokens":40335,"output_tokens":679,"output_tokens_details":{"thinking_tokens":121},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1910},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsAssh2aJxDme89vrp4","requestRef":"e1e8e727-0f98-4c9f-838e-068505e25526","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ba679068-3be4-4369-811d-6401f37467f8","timestamp":"2026-10-30T05:25:44.719Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}75{"parentUuid":"ba679068-3be4-4369-811d-6401f37467f8","isSidechain":false,"promptId":"45e9e2fb-0381-47a8-a582-641109e4c157","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LqWnzYUmbZqPpemfx8Zu4Q","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0282/out/state/gate_receipts/20261030T052544.808341Z.json\n20261030T052544.808341Z.json","is_error":false}]},"uuid":"09874687-38c3-413c-b453-0afd7295209b","timestamp":"2026-10-30T05:25:44.819Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0282/out/state/gate_receipts/20261030T052544.808341Z.json\n20261030T052544.808341Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ba679068-3be4-4369-811d-6401f37467f8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}76{"type":"api-request-blob","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","hash":"412b208c19d6f54bd6e24da0b40d83ee1dc50bc8ddb3950678c5c3be1a54d8de","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS3QgKEAgTGAI4AUIIdGhpbmtpbmcSDASSLRwtmkjPPKDlBhoMrTGVaOoRpxi87XB0IjAs9X50One64rIA9rk8eqhJLgQvKiCkM9HUipWcY3oUsBGyIuWGEsCiBlerJapRnasq+gfsPb7kCFgugX4S3ElCmIfNzQjUobDtbWIMGcrxF0zND+ZfHVEJHg+R4X1rBMScoXcxJo9XVvh2u73J7wqmlFwoMM3OjyE4aQd1rDtOs0VRvV2xBdgmUQoHqmlY/07LXjeOl1d2Kiyjb+wnFXBSQAtBfaOPv7LULmEYvh3jkkpiUkNZgMX2jKAwaOFbcb5QLZ5B/Kk/E+/xZmnz5sJELLfb8u5VhiJ0WzwItGJ+rv+6MrQLj0MWgHdDBDxe3TcMdPX4Y7RvhvNzshUm6rd/MbLmPiaEatOezLPrqHpBMyiMPaPM15D1b/mCPf8Voj1t1KJo+owE4IEljr6fTnWgT8Tfv81dUsNpfq+oem4N/ncmDr6H1miKnsEOQ88DQLfq/cxluCB0ZjPXUN1dRwGOOmuoYUmN7DJjJVHVrClMk9bCgu2d34j03UwQqAMIOEDdUR3pQami/gEmEmh6mNAfrqJnEI++iqm/fDgDob2Oe2LtOfUQoYOreyp2ew2ooLb3EZTGbjdMGoNdofEKoQGTm8XdZEFVB0pf3q6HBsfXWPHB7allRxNWgBY8Zulc/Nza+DkbsEEPpyuQtHroXs+aBTTNcMzvGbDlBFbDJFkzCpHSCfwLtBEvkjW2SiQtOFXmiIRCmkzIc2ebyf6OeBCjdlxynyf1/H6WqIrkcNwFZK0leDzFiCn9937Ro9+qQ0s3ih8DpDA5S5cMlvHafHoHl1hfrzidZMTJz91t1v1dgYUd5Ij1F8KkoW6euJc1S/OHYAyLP/nr0p+6EIpZavIR9oL9F0jpyqS8SfaUgFvfDsJUAAHptzt2G4sZWmfiZ7hvU6/Kv1WNBM1X9IQZlKvsWEewxwtoDDlfQyvtQl1XFtnQLfXahfZ/46mKGtT9FWWaedNd2gja0X8u8nPnzf8DIbViO7KdCrsTRrv/DOXK6wJFEiOhKOT5N6Ay3W7VCkMpYSfsrkHhaBN4gpXVCic2FrqeD0DIRT9dmeKb3Zi6/nh25kscTXmHhBjZPE6pQZZM2SdJ4n1ccMJKQPHQYn72Hc6kK6qUKk4EXePvgish7ae/v3dBbSblzmBMpV1MRcnJY5VBkOTOkqkOY1Yu3fAJv7qBJeaKLWE4w+TjbY1Kao2dgn91FHDnQZn6LsGSxjqBRJ0szqc+O/LZJrf51gyORln8Vh0nBi5W5UjXUDLtgJMMhp3PN5uyvKPq0j4S83RKYVTKsVfhkr2iUED8y6Q84QzkZ9cIm0EqTdPfzCeAsJ8aE2qsLSV3O46mey2G4+6km5gCKbdpKyAOQ9Uy/5N5wUKYsINPR7BD0NAJ3Q+vXQcELVECyo8dOf2iW16AyRt4CxfZ7TaUa/sAV/5oGAE="},{"type":"tool_use","id":"toolu_01LqWnzYUmbZqPpemfx8Zu4Q","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 data files match README/bundle\",\"line counts recorded (10/134/58)\",\"9 anomalies documented in data_audit.md\",\"exclusions.csv: 25 items, 0 removed\"],\"notes\":\"Table 6 SE (0.210) preferred over Table A19 SE (0.218) per key definition\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"Baseline = direct verbatim extraction of printed values from the named tables (the estimands are the printed numbers; no simpler estimate exists); values recorded under the required keys\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"out/analysis.py parses data/ only, prints matched source lines, no randomness (random_seed null)\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"17 consistency checks PASS (see checks.md)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed: extraction script written; fixed se_reported false-positive (matched editor header).\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"}}]}}77{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"59ff88b1-6e54-4796-b0a2-f1678eac523c","timestamp":"2026-10-30T05:25:44.822Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"e1e8e727-0f98-4c9f-838e-068505e25526","keep":22,"tail":["412b208c19d6f54bd6e24da0b40d83ee1dc50bc8ddb3950678c5c3be1a54d8de","c180055ae438e08b3d8e755ac3c818188d0edd86797e7518100c0670a408d879"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d11f01717c28242a8b7d42705f0a11d711de3a7f8e0f429135b6d2ffebdf96a3"}78{"parentUuid":"09874687-38c3-413c-b453-0afd7295209b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBRNXLA3TxJScCVgHW","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1806,"cache_read_input_tokens":42245,"output_tokens":4312,"output_tokens_details":{"thinking_tokens":59},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1806},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":626,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsBR7PaGnak7vTWLuWQ","requestRef":"59ff88b1-6e54-4796-b0a2-f1678eac523c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9c270961-877d-4566-a39f-95b06ed1b732","timestamp":"2026-10-30T05:25:47.136Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}79{"parentUuid":"9c270961-877d-4566-a39f-95b06ed1b732","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBRNXLA3TxJScCVgHW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VHGAaJ6f75dTUcXBjnFUgx","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract, exactly as printed, specified treatment-effect estimates, standard errors (SEs), confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1, a staggered DiD study of drug consumption room (DCR) openings in Switzerland (Tables 6 and 7), and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, an RCT of Rhode Island's RESEA programme (Table 2). Attach a locator to each value, derive a 95% CI from a printed SE and an SE from a printed CI, and flag the requested quantity that the source does not print. Nothing is re-estimated.\n\n## Data and exclusions\nInputs: the two source extracts and `data/README.md`, all SHA-256-verified against the README/bundle (`out/state/data_audit.md`). Line counts: `n_input_rows` (README.md 10, arxiv-2609.27732v1.txt 134, arxiv-2606.14621v1.txt 58). I assessed 25 requested items (`n_analysed`) and excluded none (`out/exclusions.csv`). I logged eight input problems in `data_issues`. The ones that matter most:\n- The 5 km CS-DR ATT has SE 0.210 in Table 6 but 0.218 in Table A19. I used Table 6 because the key names it.\n- Table A19's note says there are 640 never-treated regions; Tables 6 and 7 say 608.\n- For the fatal 5 km effect, the abstract says 35% and Section 6.1 says about 30%.\n- The prose gives the weeks-on-UI CI as positive magnitudes, while Table 2 prints negative bounds.\n- One paragraph has a rendering defect: \"p ¡ 0.001\".\n\n## Method\n`out/analysis.py` parses the \" | \"-delimited table rows. In the DCR tables, the SE is the parenthesised value on the row after each coefficient. The script removes \"$\", thousands separators, units and significance stars, keeps the printed sign, and prints every matched source line. Locators follow `<source> | Table n | row \"…\" | column \"…\"`. Significance at 10% is read from the stars (`*` means p<0.10). The script searches the extract for an exact p-value and finds none. Derivations use the normal critical value 1.96 (`derivation_notes`). There is no randomness (`random_seed` = null). The baseline is simply the direct extraction, since the estimands are the printed numbers.\n\n## Results\nNote that JSON drops trailing zeros (0.210 is stored as 0.21). The verbatim strings are kept in `printed_strings`.\n\n**DCR (arxiv-2609.27732v1)**\n- Table 6, overall ATT:\n  - 5 km: −0.431** with SE 0.210 (`dcr_att_5km`, `dcr_att_5km_se`).\n  - 3 km: −0.283 with SE 0.279 (`dcr_att_3km`, `dcr_att_3km_se`).\n  - Observations at 5 km: 17,250 (`dcr_obs_5km`).\n- Table 7, by outcome:\n  - Narcotics at 3 km: 0.549*** with SE 0.187 (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`).\n  - Drug-related (fatal) at 1 km: −1.207*** with SE 0.137 (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`).\n  - HIV at 5 km: −0.340 with SE 0.294 and no star (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`). It is therefore not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false). The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n- Derived 95% CI for the Table 6 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA (arxiv-2606.14621v1, Table 2)**\n- Wage ATE: $1,153 per year, 95% CI [166, 2,141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`).\n  - No SE is printed (`ri_wage_ate_se_reported` = false).\n  - The SE implied by the CI is 503.83 (`ri_wage_ate_se_derived`).\n- Control-group mean wage: $33,043 (`ri_control_mean_wage`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks on UI ATE: −1.99, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\n## Uncertainty\n- **SEs:** all DCR uncertainties (Tables 6 and 7) are SEs clustered at the MedStat region level, printed in parentheses (`dcr_*_se`). The stars use the source's thresholds: *** p<0.01, ** p<0.05, * p<0.10.\n- **CIs:** the RESEA uncertainties are two-sided 95% CIs, printed with p-values. They come from the \"methodology described in the main text\", which is not part of the extract.\n- **Derived DCR CI:** `dcr_att_5km_ci_low`/`_high` = ATT ± 1.96·SE. This assumes the estimator is approximately normal with the printed clustered SE. With a t critical value or 2.0 the interval would be slightly wider. The interval excludes 0, which agrees with the ** marking (|t| ≈ 2.05; `out/state/checks.md`).\n- **Derived RESEA SE:** `ri_wage_ate_se_derived` = (2,141 − 166)/(2·1.96). This assumes a symmetric normal-approximation CI. The printed CI is symmetric to within 1 USD of rounding. Plugging the derived SE back in gives a two-sided p of about 0.022, matching the printed p=0.022 (checks.md).\n- Inputs are printed to only 3 decimals (DCR) or whole dollars (RESEA), so the derived values carry that rounding error.\n\n## Limitations and open issues\n- The source does not print the exact p-value of the HIV 5 km ATT. It cannot be obtained without a distributional assumption, so I left it null. Its absence is the expected \"not reported\" item.\n- The task is otherwise complete (`feasibility` = \"complete\").\n- Using Table A19's SE (0.218) instead of Table 6's would give a different derived CI. The source does not explain the discrepancy, and I did not resolve it.\n- The method behind the RESEA CI is not in the extract, so the derived SE depends on the normality and symmetry assumption.\n- I found the internal inconsistencies listed under `data_issues` but did not adjudicate them. None of them changes a requested key.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km overall ATT of -0.431 (**) with clustered SE 0.210, a 3 km ATT of -0.283 (SE 0.279) and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same 5 km CS-DR estimate; Table 6 used as the key requires.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km ATT 0.549 (SE 0.187), fatal 1 km ATT -1.207 (SE 0.137) and HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"JSON numbers drop trailing zeros; verbatim strings in printed_strings.\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and no exact p-value is printed, so the p-value is reported as null.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"A p-value could be computed under normality but that would be an assumption, not a printed value.\"},\n {\"id\":\"C4\",\"text\":\"Under a normal 1.96 convention the 95% CI of the Table 6 5 km ATT is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"dcr_att_5km\",\"dcr_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; depends on normal approximation and on using the Table 6 SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], no SE, and a control mean wage of $33,043; the implied SE under a symmetric 1.96 convention is about 503.83.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric normal CI; the CI methodology is not in the extract.\"},\n {\"id\":\"C6\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"The prose reports the weeks CI as positive magnitudes; table signs used.\"},\n {\"id\":\"C7\",\"text\":\"The extracts contain internal inconsistencies (SE 0.210 vs 0.218, 608 vs 640 control regions, 35% vs ~30% fatal reduction, sign convention of the weeks CI in prose, a rendering defect), none of which changes a requested key.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Inconsistencies reported, not resolved.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"17 consistency checks PASS (see checks.md)\",\"gate run 20261030T052544.808341Z: all key/type/reproducibility checks ok; failures only for P5 deliverables (report, claims) and non-terminal P4/P5 — expected at this point\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T052544.808341Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 7 claims, all keys exist\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1 receipt 20261030T052544.808341Z); P5 report+claims written.\" >> 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> out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nExtract, exactly as printed, specified treatment-effect estimates, standard errors (SEs), confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1, a staggered DiD study of drug consumption room (DCR) openings in Switzerland (Tables 6 and 7), and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, an RCT of Rhode Island's RESEA programme (Table 2). Attach a locator to each value, derive a 95% CI from a printed SE and an SE from a printed CI, and flag the requested quantity that the source does not print. Nothing is re-estimated.\n\n## Data and exclusions\nInputs: the two source extracts and `data/README.md`, all SHA-256-verified against the README/bundle (`out/state/data_audit.md`). Line counts: `n_input_rows` (README.md 10, arxiv-2609.27732v1.txt 134, arxiv-2606.14621v1.txt 58). I assessed 25 requested items (`n_analysed`) and excluded none (`out/exclusions.csv`). I logged eight input problems in `data_issues`. The ones that matter most:\n- The 5 km CS-DR ATT has SE 0.210 in Table 6 but 0.218 in Table A19. I used Table 6 because the key names it.\n- Table A19's note says there are 640 never-treated regions; Tables 6 and 7 say 608.\n- For the fatal 5 km effect, the abstract says 35% and Section 6.1 says about 30%.\n- The prose gives the weeks-on-UI CI as positive magnitudes, while Table 2 prints negative bounds.\n- One paragraph has a rendering defect: \"p ¡ 0.001\".\n\n## Method\n`out/analysis.py` parses the \" | \"-delimited table rows. In the DCR tables, the SE is the parenthesised value on the row after each coefficient. The script removes \"$\", thousands separators, units and significance stars, keeps the printed sign, and prints every matched source line. Locators follow `<source> | Table n | row \"…\" | column \"…\"`. Significance at 10% is read from the stars (`*` means p<0.10). The script searches the extract for an exact p-value and finds none. Derivations use the normal critical value 1.96 (`derivation_notes`). There is no randomness (`random_seed` = null). The baseline is simply the direct extraction, since the estimands are the printed numbers.\n\n## Results\nNote that JSON drops trailing zeros (0.210 is stored as 0.21). The verbatim strings are kept in `printed_strings`.\n\n**DCR (arxiv-2609.27732v1)**\n- Table 6, overall ATT:\n  - 5 km: −0.431** with SE 0.210 (`dcr_att_5km`, `dcr_att_5km_se`).\n  - 3 km: −0.283 with SE 0.279 (`dcr_att_3km`, `dcr_att_3km_se`).\n  - Observations at 5 km: 17,250 (`dcr_obs_5km`).\n- Table 7, by outcome:\n  - Narcotics at 3 km: 0.549*** with SE 0.187 (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`).\n  - Drug-related (fatal) at 1 km: −1.207*** with SE 0.137 (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`).\n  - HIV at 5 km: −0.340 with SE 0.294 and no star (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`). It is therefore not marked significant at 10% (`dcr_hiv_att_5km_significant_10pct` = false). The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n- Derived 95% CI for the Table 6 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA (arxiv-2606.14621v1, Table 2)**\n- Wage ATE: $1,153 per year, 95% CI [166, 2,141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`).\n  - No SE is printed (`ri_wage_ate_se_reported` = false).\n  - The SE implied by the CI is 503.83 (`ri_wage_ate_se_derived`).\n- Control-group mean wage: $33,043 (`ri_control_mean_wage`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks on UI ATE: −1.99, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\n## Uncertainty\n- **SEs:** all DCR uncertainties (Tables 6 and 7) are SEs clustered at the MedStat region level, printed in parentheses (`dcr_*_se`). The stars use the source's thresholds: *** p<0.01, ** p<0.05, * p<0.10.\n- **CIs:** the RESEA uncertainties are two-sided 95% CIs, printed with p-values. They come from the \"methodology described in the main text\", which is not part of the extract.\n- **Derived DCR CI:** `dcr_att_5km_ci_low`/`_high` = ATT ± 1.96·SE. This assumes the estimator is approximately normal with the printed clustered SE. With a t critical value or 2.0 the interval would be slightly wider. The interval excludes 0, which agrees with the ** marking (|t| ≈ 2.05; `out/state/checks.md`).\n- **Derived RESEA SE:** `ri_wage_ate_se_derived` = (2,141 − 166)/(2·1.96). This assumes a symmetric normal-approximation CI. The printed CI is symmetric to within 1 USD of rounding. Plugging the derived SE back in gives a two-sided p of about 0.022, matching the printed p=0.022 (checks.md).\n- Inputs are printed to only 3 decimals (DCR) or whole dollars (RESEA), so the derived values carry that rounding error.\n\n## Limitations and open issues\n- The source does not print the exact p-value of the HIV 5 km ATT. It cannot be obtained without a distributional assumption, so I left it null. Its absence is the expected \"not reported\" item.\n- The task is otherwise complete (`feasibility` = \"complete\").\n- Using Table A19's SE (0.218) instead of Table 6's would give a different derived CI. The source does not explain the discrepancy, and I did not resolve it.\n- The method behind the RESEA CI is not in the extract, so the derived SE depends on the normality and symmetry assumption.\n- I found the internal inconsistencies listed under `data_issues` but did not adjudicate them. None of them changes a requested key.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km overall ATT of -0.431 (**) with clustered SE 0.210, a 3 km ATT of -0.283 (SE 0.279) and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same 5 km CS-DR estimate; Table 6 used as the key requires.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km ATT 0.549 (SE 0.187), fatal 1 km ATT -1.207 (SE 0.137) and HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"JSON numbers drop trailing zeros; verbatim strings in printed_strings.\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and no exact p-value is printed, so the p-value is reported as null.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"A p-value could be computed under normality but that would be an assumption, not a printed value.\"},\n {\"id\":\"C4\",\"text\":\"Under a normal 1.96 convention the 95% CI of the Table 6 5 km ATT is [-0.8426, -0.0194].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"dcr_att_5km\",\"dcr_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; depends on normal approximation and on using the Table 6 SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], no SE, and a control mean wage of $33,043; the implied SE under a symmetric 1.96 convention is about 503.83.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric normal CI; the CI methodology is not in the extract.\"},\n {\"id\":\"C6\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"The prose reports the weeks CI as positive magnitudes; table signs used.\"},\n {\"id\":\"C7\",\"text\":\"The extracts contain internal inconsistencies (SE 0.210 vs 0.218, 608 vs 640 control regions, 35% vs ~30% fatal reduction, sign convention of the weeks CI in prose, a rendering defect), none of which changes a requested key.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Inconsistencies reported, not resolved.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"17 consistency checks PASS (see checks.md)\",\"gate run 20261030T052544.808341Z: all key/type/reproducibility checks ok; failures only for P5 deliverables (report, claims) and non-terminal P4/P5 — expected at this point\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T052544.808341Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json 7 claims, all keys exist\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1 receipt 20261030T052544.808341Z); P5 report+claims written.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\""}}]}}84{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"878ecaa3-baeb-4d9c-a2ac-e7c323db08a3","timestamp":"2026-10-30T05:26:20.032Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"59ff88b1-6e54-4796-b0a2-f1678eac523c","keep":24,"tail":["249dab8a837a5ed5ac2a0f5b06cbfdf23b5ca3ab01f0ca36c9285c8cc439bd63","705c9b418f38ac19f32d6f126f1468654d67771d141513a1df23ca0d79e0a4d5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"948f4dd8aa8bad7bf85c2d0955311bb0d5db8d72b362f42376ff268d443a2beb"}85{"parentUuid":"103acd28-5d39-4a7d-8faa-cfc000d549d9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsE1vxT6G15hV9UYAkX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018sv7GH5dW91AJSjv2m6Gxu","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'][2]=\"final gate run 20261030T052620.019566Z: PASSED (0 of 48 failed)\"\np['P5']['notes']=\"gate receipt out/state/gate_receipts/20261030T052620.019566Z.json\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed; 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Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1"}}]}}88{"type":"api-request","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","id":"94cdfa8e-38ae-4456-98b4-d6f434444141","timestamp":"2026-10-30T05:26:24.216Z","version":"2.1.314","querySource":"sdk","shapeHash":"9a8a75928677d7977854436270e9141ed7a9d542d814207e480379499fe23104","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"878ecaa3-baeb-4d9c-a2ac-e7c323db08a3","keep":26,"tail":["b445c41b2675bc717bc8d8b2989649deafbde11f944d50cb548c48f50dc6225f","077726997ec143f68b94c1a9e5fa6b37563d241f23799818f6a094dcf71835f3"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a9052a8cf819cf0e90d4ddb27f2aa3cc790cbcceb390d4d78b5f6fad18358526"}89{"parentUuid":"75212eac-562d-4684-9023-db6d6885f60a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsEKsT4WNJUnNTUnxwz","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":337,"cache_read_input_tokens":48458,"output_tokens":987,"output_tokens_details":{"thinking_tokens":14},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":337},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6390,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsEKabLr1Dxf4Ewt4r5","requestRef":"94cdfa8e-38ae-4456-98b4-d6f434444141","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c758de61-c45d-4bff-8fca-0814f9f3ffd2","timestamp":"2026-10-30T05:26:32.364Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}90{"parentUuid":"c758de61-c45d-4bff-8fca-0814f9f3ffd2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsEKsT4WNJUnNTUnxwz","type":"message","role":"assistant","content":[{"type":"text","text":"I've extracted all the requested values from the two papers, and the final gate check passed (0 of 48 checks failed). Running `out/analysis.py` again from a clean state rebuilt `out/results.json` identically.\n\n**Values as printed**\n- **Drug consumption rooms, Table 6:** the 5 km effect is −0.431** with standard error 0.210, and the 3 km effect is −0.283 (0.279). There are 17,250 observations at 5 km.\n- **Drug consumption rooms, Table 7:**\n  - Narcotics at 3 km: 0.549*** (0.187).\n  - Fatal at 1 km: −1.207*** (0.137).\n  - HIV at 5 km: −0.340 (0.294). It has no star, so it is not marked significant at 10%.\n- **Rhode Island RESEA, Table 2:**\n  - Wage effect: $1,153 a year, 95% CI [166, 2,141]. No standard error is printed.\n  - Control-group mean wage: $33,043.\n  - Reemployment: 1.47 percentage points.\n  - Weeks on unemployment insurance: −1.99, CI [−2.24, −1.75].\n\n**The quantity that isn't reported:** the exact p-value for the HIV 5 km effect is not printed anywhere in the extract, so it is `null`.\n\n**Derived values (normal approximation, 1.96):**\n- **95% CI for the 5 km drug-room effect:** [−0.8426, −0.0194]. It excludes zero, which agrees with the ** marking.\n- **Standard error of the wage effect:** about 503.83. It assumes the printed CI is symmetric; it is, to within $1 of rounding. Plugging it back in gives p ≈ 0.022, matching the printed p.\n\n**Problems in the sources** (logged in `data_issues`; none changes a requested value):\n- The 5 km standard error is 0.210 in Table 6 but 0.218 in appendix Table A19. I used Table 6 because the task names it, but the derived CI would differ with the other figure, and the source doesn't explain the gap.\n- Table A19 says there are 640 comparison regions; Tables 6 and 7 say 608.\n- For the fatal 5 km effect, the abstract says 35% and Section 6.1 says about 30%.\n- The RESEA text gives the weeks-on-insurance CI as positive numbers, while the table prints negative ones.\n- One RESEA paragraph has a garbled character (\"p ¡ 0.001\").\n\nJSON drops trailing zeros (0.210 is stored as 0.21), so I added a `printed_strings` field that keeps the exact text for each value.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json` (7 claims)\n- `exclusions.csv` (25 items assessed, none excluded)\n- `state/` (working notes, checks and gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":337,"cache_read_input_tokens":48458,"output_tokens":987,"output_tokens_details":{"thinking_tokens":14},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":337},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsEKabLr1Dxf4Ewt4r5","requestRef":"94cdfa8e-38ae-4456-98b4-d6f434444141","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"80e8a4d1-298e-4838-ae96-3dd7f15ff8f6","timestamp":"2026-10-30T05:26:33.663Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0282","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","version":"2.1.314","gitBranch":"HEAD"}91{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"80e8a4d1-298e-4838-ae96-3dd7f15ff8f6","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286"}92{"type":"cost-state","sessionId":"f9afc518-6079-45da-9966-a1b4e7634286","totalCostUSD":0.6978822,"totalAPIDuration":185911,"totalAPIDurationWithoutRetries":185900,"totalToolDuration":1193,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":187237,"startTime":1793337806437,"modelUsage":{"claude-opus-5-5":{"inputTokens":30,"outputTokens":19222,"thinkingTokens":1814,"cacheReadInputTokens":384336,"cacheCreationInputTokens":47291,"cacheCreationEphemeral5mInputTokens":47291,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.6978822}},"hasUnknownModelCost":false}93