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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\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194"}12{"parentUuid":"cdf2abc0-1e2d-426b-a0fe-9385a7f7d1fc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs2PYNfnEmRErLdaxny","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":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":129,"output_tokens_details":{"thinking_tokens":21},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":454,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs2PG1RQpBNixE44BwL","requestRef":"8fb63071-3a62-4da5-9a6a-62ba35e057ec","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"20540f74-c226-4d0d-93d1-973ff1205ce2","timestamp":"2026-10-30T05:23:43.622Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"20540f74-c226-4d0d-93d1-973ff1205ce2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs2PYNfnEmRErLdaxny","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017ktyYrn14bnfLhR3YXaJA3","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  161 simon-pierreboucher  staff  5152 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\n\n./data:\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\n\n./data/sources:\ntotal 64\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 .\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  13405 oct.  29 23:45 arxiv-2606.14621v1.txt\n-rw-r--r--  1 simon-pierreboucher  staff  12401 oct.  29 23:45 arxiv-2609.27732v1.txt\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"97b04787-ae4e-466a-b37e-dd66d832b094","timestamp":"2026-10-30T05:23:44.362Z","toolUseResult":{"stdout":"# 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  161 simon-pierreboucher  staff  5152 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\n\n./data:\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\n\n./data/sources:\ntotal 64\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 .\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:45 ..\n-rw-r--r--  1 simon-pierreboucher  staff  13405 oct.  29 23:45 arxiv-2606.14621v1.txt\n-rw-r--r--  1 simon-pierreboucher  staff  12401 oct.  29 23:45 arxiv-2609.27732v1.txt\n(eval):1: no matches found: out/state/*","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ad535323-3301-48f6-8164-58f8c78cf0d1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"716eb8f11fa54431e9298e970ed9340b56d721ffb26cbd4167e02886db8346e9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017ktyYrn14bnfLhR3YXaJA3","name":"Bash","input":{"command":"cat TASK.md; 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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. 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     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\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      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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. 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     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\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      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    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\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  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","is_error":false}]},"uuid":"e3504efe-6eff-4530-9bbd-4c7a194fd0f3","timestamp":"2026-10-30T05:23:47.324Z","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\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  data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b  data/sources/arxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bc6557dc-23ed-4956-9832-1b677c6ac498","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}23{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"d0dc4eb7535cbfeb326cb085a39cccdaf93898209da1da9f781b4a7cccf76286","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017DV1bu1Uea91DsXBoEzA3M","name":"Bash","input":{"command":"cat data/README.md; cat -n data/sources/arxiv-2609.27732v1.txt; cat gate_spec.json bundle.json","description":"Read README, DCR source, gate spec"}},{"type":"tool_use","id":"toolu_01C8mb6P5vUx6j9WCMdX2zXN","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*","description":"Read RI source, line counts, hashes"}}]}}24{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"147c32780f8bfb2ebd450324c07f567a55f5092dd56fcc8a39bc7e0939d71508","message":{"role":"user","content":[{"tool_use_id":"toolu_017DV1bu1Uea91DsXBoEzA3M","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     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\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      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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) . 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Extract, exactly as printed, specified treatment-effect point estimates, SEs, CI bounds and counts from two arXiv extracts (Armendariz 2026 DCR staggered DiD, Tables 6/7; Li et al. 2026 RESEA RCT, Table 2), attach locators, derive (a) a 95% CI for the Table 6 5 km ATT from its SE and (b) an SE for the Table 2 wage ATE from its 95% CI, stating the convention, and flag the quantity not reported (exact p-value of HIV 5 km ATT).\n**Estimands.** The printed numbers themselves (nothing re-estimated). Derived quantities are meaningful under approximate normality of the estimator (symmetric Wald interval, critical value 1.96).\n**Outputs.** out/results.json (keys listed in TASK.md: 11 DCR printed values, dcr_hiv_att_5km_p_value (null if not printed), dcr_hiv_att_5km_significant_10pct, derived dcr_att_5km_ci_low/high, ri_wage_ate + CI, ri_wage_ate_se_reported, derived ri_wage_ate_se_derived, ri_control_mean_wage, ri_reemployment_ate_pp, ri_weeks_ui_ate + CI, locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed=null); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules.** Only the two extracts are evidence; values as printed (sign, decimals) from the table named in the key; derived keys use printed values of the named table with stated convention; locator format `<source id> | Table <n> | row \"<..>\" | column \"<..>\"`; unreported → null + \"not reported in <source>\".\n**Invalidity.** Using memory/other versions; using a value from a different table (e.g. Table A19 SE 0.218 instead of Table 6 SE 0.210; Table 3 instead of Table 2; text instead of table); rounding; fabricating an unprinted p-value.\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","description":"Write contract, phase ledger, 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Derived quantities are meaningful under approximate normality of the estimator (symmetric Wald interval, critical value 1.96).\n**Outputs.** out/results.json (keys listed in TASK.md: 11 DCR printed values, dcr_hiv_att_5km_p_value (null if not printed), dcr_hiv_att_5km_significant_10pct, derived dcr_att_5km_ci_low/high, ri_wage_ate + CI, ri_wage_ate_se_reported, derived ri_wage_ate_se_derived, ri_control_mean_wage, ri_reemployment_ate_pp, ri_weeks_ui_ate + CI, locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed=null); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules.** Only the two extracts are evidence; values as printed (sign, decimals) from the table named in the key; derived keys use printed values of the named table with stated convention; locator format `<source id> | Table <n> | row \"<..>\" | column \"<..>\"`; unreported → null + \"not reported in <source>\".\n**Invalidity.** Using memory/other versions; using a value from a different table (e.g. Table A19 SE 0.218 instead of Table 6 SE 0.210; Table 3 instead of Table 2; text instead of table); rounding; fabricating an unprinted p-value.\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","description":"Write contract, phase ledger, 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-c \"\nfor t,o in [(7,16175),(35,16875),(50,17250)]: print(o/25, o/25-t, (608+t)*25)\nfrom math import erf,sqrt\np=lambda z:2*(1-0.5*(1+erf(abs(z)/sqrt(2))))\nprint(p(0.431/0.210),p(0.431/0.218),p(0.340/0.294),p(1153/((2141-166)/3.92)))\n\"","description":"Check observation counts and implied p-values"}}]}}37{"type":"api-request","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","id":"dba714b7-638d-496d-afe0-3c9ce38b3180","timestamp":"2026-10-30T05:24:14.194Z","version":"2.1.314","querySource":"sdk","shapeHash":"6cadd754b990b914a32307d3fb20fcdbaf3f1a460ab21338160883bccbb38440","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":"cc2569d0-b40c-4a28-b08b-8665dff9073d","keep":8,"tail":["2ecfd14841d1bfe2527cf4dadb914a340590b01ec8c4c38f31673fd66b60923e","8d2bf58519fbaa4bf74286ebdce276b2c109d80eba5e0462356149317d830236"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"78425a066dafe87bc1af2afd0acebc5d0de649e9862cfaff0b2937921bb294fe"}38{"parentUuid":"6ee55c00-3625-490c-8574-55d6d7cd66be","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs4juYaZAftPTnbmQQ6","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSgwYKEAgTGAI4AUIIdGhpbmtpbmcSDPRr+nWLUb/eQXNZUhoMA3PTxG4Wrphb9OdGIjBx8rOcDXc0mbOGlaX4omxS6aPPCgxtrWF+UwdXe8B6YNMxTxDgb/b4HXQLzzwB9akqoAXg8D6mHihvtoKp9yzCbsTZaBASz+4N+va+JWJvUYnNb201Q+Y+ET00Ra7RICIqvV4TrUQuAJegT4pjKwowKxZOjughFB1WUaJB6jAk0BMjAB5tm1XNEjJEWZ/v+/m76Tx1fW+iSh/maIfHusvBxyP3KI/nmY6CxF7mx5ohmdelBGmAYw68BKNaQMJNjROT/QXxllvA3Ddh1RM6UyjmvuaJQmw52NbM10LmkreZLOmTBSl/pusfQ/vjMM6S041noa3g3rOQ+yIP5CieOvsW79pyVLYZIQ7BpxpltYTyNpnk0ANygYvF9AOo8WcH4yKj/lpTexQomU5RybZBL/0upMeljFaZ9vNjMjjHcwJMF0qghiK+hIv+D1LHGq1cL8yyJQ8aznFJuFD9tqq3P0dVPfo9cAeaZWI+ZSgCRImrZhTvKTio0z48t0yJ9rebGtlKGWfor7POdNJyQmox8RajZ8SJxdAZ6s4dkhu7SDt5y83Adu1Outm/cvNf8Hn0VueMiImRrHHxMF+TqfZcyTLmnC/lJOzmomlXShSrYttRH1trCz1bfXU6Aq3nXnUdyktyml4Mgn2gsUVGmdXEHWbTwcCgUvPcui5XP6N1EbnOMfJwSbF8+/Ril2V2mc03esASKCjeB0XLp5GaBU1hfxxVA2avf3RF+42saQIAAQnPrzKF5nXX96/Hc+TP5tIyeAz21I7yGMxcjU8bQNUQEpRnC11DBWts+n8wQ97XbhjRJLsdW1lr3GYZJ4LgUOf08p6ZNfc3e5VKBmyHJyiI3RCIALeNGRUfY2nHD+TGWE0BesZDMkM0yKW/FVYCtge3OOjanomLiSReC70ACjmcCnTbJi9wIyZ3Fz6BR1KhIqiYCKZC2OVvsYauvaim0HfUUwsfo+0YAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":453,"cache_read_input_tokens":24058,"output_tokens":1444,"output_tokens_details":{"thinking_tokens":20},"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":453},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":105,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs4jdfcUzBtmGWzgNTs","requestRef":"dba714b7-638d-496d-afe0-3c9ce38b3180","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"40d5caa3-f677-4273-b8d6-7e33cfe4be4c","timestamp":"2026-10-30T05:24:15.713Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286/out/state","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"40d5caa3-f677-4273-b8d6-7e33cfe4be4c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs4juYaZAftPTnbmQQ6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014Zj4dnwpm2XTsYQDh7pZ1p","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR extract: Table 6 (cols 1/3/5 km; rows ATT, SE line, % change, Treated regions, Control regions, Observations), Table 7 (rows Mental/Behavioral, Hepatitis, HIV, Narcotics, Drug-related (fatal), each followed by SE line; Treated/Control regions), Table A19 (5 km, 4 estimators). RESEA extract: Table 2 (6 outcomes x Treatment|Control|T-C difference|ATE, each effect cell \"estimate; p=..; 95% CI: [lo, hi]\"), Table 3 (3 outcomes x 3 estimators). Units: log points (DCR), USD/yr, pp, weeks (RESEA). No missing cells among requested values; no duplicate rows.\n\n## Anomalies / internal inconsistencies\n1. DCR Table 6 5 km ATT SE printed (0.210) while Table A19 CS-DR column (same estimator, same 5 km buffer, same ATT -0.431**, same 17,250 obs) prints (0.218). Handling: use Table 6 as the key requires; record as data issue.\n2. DCR control-region count: Table 6/7 and Fig. 6 note say 608 never-treated regions; Table A19 note says 640. Observations in Table 6 equal (640 + treated) x 25 for all three columns (16,175 = 647x25; 16,875 = 675x25; 17,250 = 690x25), not (608 + treated) x 25. Handling: report Observations as printed; flag.\n3. DCR prose: abstract says fatal outcomes \"fall by 35%\" whereas Table 7 5 km fatal ATT -0.354 corresponds to ~-30% (Section 6.1 says ~30%); prose says HIV estimates \"close to zero\" while 5 km HIV ATT is -0.340 (insignificant). Handling: tables used; noted.\n4. RESEA prose gives the weeks-on-UI CI as magnitudes \"[1.75 weeks, 2.24 weeks]\" while Table 2 prints [-2.24, -1.75]. Handling: Table 2 used, sign as printed.\n5. RESEA rendering defect: \"p ¡ 0.001\" in Section 3 text (should be p < 0.001). Not used for any key.\n6. RESEA Tables 2-3 re-set by extract editor (stated in header); values declared verbatim. Table 3 col 1 duplicates Table 2 ATE column (checked equal for Wage).\n\n## Information-availability\nNo input violates the rules; only the two extracts used. README used only for provenance. No exclusions/filters applied (exclusion ledger has a single no-op row).\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,\"none: all 22 requested source items extracted; no filtering\",22,22,0,\"extraction task; no records excluded\"\\n' > exclusions.csv\necho \"- 2026-10-30 P1 passed: audit + exclusions written; 6 anomalies logged.\" >> state/log.md","description":"Write data audit and exclusion ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":453,"cache_read_input_tokens":24058,"output_tokens":1444,"output_tokens_details":{"thinking_tokens":20},"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":453},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs4jdfcUzBtmGWzgNTs","requestRef":"dba714b7-638d-496d-afe0-3c9ce38b3180","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bfc4f8ef-caaa-46a9-8fb7-b1bae936292c","timestamp":"2026-10-30T05:24:28.273Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286/out/state","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"bfc4f8ef-caaa-46a9-8fb7-b1bae936292c","isSidechain":false,"promptId":"af83ad24-a245-4ceb-92cd-35a6d1a3a3e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014Zj4dnwpm2XTsYQDh7pZ1p","type":"tool_result","content":"(eval):cd:1: no such file or directory: out/state","is_error":false}]},"uuid":"c01a076a-1672-49cb-a1ab-3b84cd1d420a","timestamp":"2026-10-30T05:24:28.297Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out/state","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bfc4f8ef-caaa-46a9-8fb7-b1bae936292c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286/out","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"37f20131ee1f4c93694cc0fa18b963d9550e6b1a3aed9439b5d164ee80074f38","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014Zj4dnwpm2XTsYQDh7pZ1p","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR extract: Table 6 (cols 1/3/5 km; rows ATT, SE line, % change, Treated regions, Control regions, Observations), Table 7 (rows Mental/Behavioral, Hepatitis, HIV, Narcotics, Drug-related (fatal), each followed by SE line; Treated/Control regions), Table A19 (5 km, 4 estimators). RESEA extract: Table 2 (6 outcomes x Treatment|Control|T-C difference|ATE, each effect cell \"estimate; p=..; 95% CI: [lo, hi]\"), Table 3 (3 outcomes x 3 estimators). Units: log points (DCR), USD/yr, pp, weeks (RESEA). No missing cells among requested values; no duplicate rows.\n\n## Anomalies / internal inconsistencies\n1. DCR Table 6 5 km ATT SE printed (0.210) while Table A19 CS-DR column (same estimator, same 5 km buffer, same ATT -0.431**, same 17,250 obs) prints (0.218). Handling: use Table 6 as the key requires; record as data issue.\n2. DCR control-region count: Table 6/7 and Fig. 6 note say 608 never-treated regions; Table A19 note says 640. Observations in Table 6 equal (640 + treated) x 25 for all three columns (16,175 = 647x25; 16,875 = 675x25; 17,250 = 690x25), not (608 + treated) x 25. Handling: report Observations as printed; flag.\n3. DCR prose: abstract says fatal outcomes \"fall by 35%\" whereas Table 7 5 km fatal ATT -0.354 corresponds to ~-30% (Section 6.1 says ~30%); prose says HIV estimates \"close to zero\" while 5 km HIV ATT is -0.340 (insignificant). Handling: tables used; noted.\n4. RESEA prose gives the weeks-on-UI CI as magnitudes \"[1.75 weeks, 2.24 weeks]\" while Table 2 prints [-2.24, -1.75]. Handling: Table 2 used, sign as printed.\n5. RESEA rendering defect: \"p ¡ 0.001\" in Section 3 text (should be p < 0.001). Not used for any key.\n6. RESEA Tables 2-3 re-set by extract editor (stated in header); values declared verbatim. Table 3 col 1 duplicates Table 2 ATE column (checked equal for Wage).\n\n## Information-availability\nNo input violates the rules; only the two extracts used. README used only for provenance. 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR extract: Table 6 (cols 1/3/5 km; rows ATT, SE line, % change, Treated regions, Control regions, Observations), Table 7 (rows Mental/Behavioral, Hepatitis, HIV, Narcotics, Drug-related (fatal), each followed by SE line; Treated/Control regions), Table A19 (5 km, 4 estimators). RESEA extract: Table 2 (6 outcomes x Treatment|Control|T-C difference|ATE, each effect cell \"estimate; p=..; 95% CI: [lo, hi]\"), Table 3 (3 outcomes x 3 estimators). Units: log points (DCR), USD/yr, pp, weeks (RESEA). No missing cells among requested values; no duplicate rows.\n\n## Anomalies / internal inconsistencies\n1. DCR Table 6 5 km ATT SE printed (0.210) while Table A19 CS-DR column (same estimator, same 5 km buffer, same ATT -0.431**, same 17,250 obs) prints (0.218). Handling: use Table 6 as the key requires; record as data issue.\n2. DCR control-region count: Table 6/7 and Fig. 6 note say 608 never-treated regions; Table A19 note says 640. Observations in Table 6 equal (640 + treated) x 25 for all three columns (16,175 = 647x25; 16,875 = 675x25; 17,250 = 690x25), not (608 + treated) x 25. Handling: report Observations as printed; flag. (25 = years 1998-2022 is my inference, not stated in the extract.)\n3. DCR prose: abstract says fatal outcomes \"fall by 35%\" whereas Table 7 5 km fatal ATT -0.354 corresponds to ~-30% (Section 6.1 says ~30%); prose says HIV estimates \"close to zero\" while 5 km HIV ATT is -0.340 (insignificant). Handling: tables used; noted.\n4. RESEA prose gives the weeks-on-UI CI as magnitudes \"[1.75 weeks, 2.24 weeks]\" while Table 2 prints [-2.24, -1.75]. Handling: Table 2 used, sign as printed.\n5. RESEA rendering defect: \"p ¡ 0.001\" in Section 3 text (should be p < 0.001). Not used for any key.\n6. RESEA Tables 2-3 re-set by extract editor (stated in header); values declared verbatim. Table 3 col 1 duplicates Table 2 ATE column.\n\n## Information-availability\nNo input violates the rules; only the two extracts used. README used only for provenance. No exclusions/filters applied (exclusion ledger has a single no-op row).\nEOF\ncat out/exclusions.csv","description":"Write data audit file"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":414,"cache_read_input_tokens":25979,"output_tokens":1363,"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":414},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs66R7LCDPXnMXAWuAc","requestRef":"8dde9a66-fbf2-43d8-a1d6-ca4679bc5312","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9045ce8f-45a7-4135-b8c0-78922ee1f2f1","timestamp":"2026-10-30T05:24:43.760Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"9045ce8f-45a7-4135-b8c0-78922ee1f2f1","isSidechain":false,"promptId":"af83ad24-a245-4ceb-92cd-35a6d1a3a3e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XJFJ6L8FFGJxtrQGaqWmnM","type":"tool_result","content":"step,rule,n_before,n_after,n_removed,rationale\n1,\"none: all 22 requested source items extracted; no filtering\",22,22,0,\"extraction task; no records excluded\"","is_error":false}]},"uuid":"ca87ea5d-594b-499d-aa7c-c1e9df077a69","timestamp":"2026-10-30T05:24:43.795Z","toolUseResult":{"stdout":"step,rule,n_before,n_after,n_removed,rationale\n1,\"none: all 22 requested source items extracted; no filtering\",22,22,0,\"extraction task; no records excluded\"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9045ce8f-45a7-4135-b8c0-78922ee1f2f1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"253a6fa2f180f2f4f5164a16f19c4793eb92601e261330845480a8f185b5d8f1","message":{"role":"assistant","content":[{"type":"text","text":"Data audit file was not written; redo it with absolute paths."},{"type":"tool_use","id":"toolu_01XJFJ6L8FFGJxtrQGaqWmnM","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | matches bundle.json |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain text; tables one row per line with \" | \" separators. DCR extract: Table 6 (cols 1/3/5 km; rows ATT, SE line, % change, Treated regions, Control regions, Observations), Table 7 (rows Mental/Behavioral, Hepatitis, HIV, Narcotics, Drug-related (fatal), each followed by SE line; Treated/Control regions), Table A19 (5 km, 4 estimators). RESEA extract: Table 2 (6 outcomes x Treatment|Control|T-C difference|ATE, each effect cell \"estimate; p=..; 95% CI: [lo, hi]\"), Table 3 (3 outcomes x 3 estimators). Units: log points (DCR), USD/yr, pp, weeks (RESEA). No missing cells among requested values; no duplicate rows.\n\n## Anomalies / internal inconsistencies\n1. DCR Table 6 5 km ATT SE printed (0.210) while Table A19 CS-DR column (same estimator, same 5 km buffer, same ATT -0.431**, same 17,250 obs) prints (0.218). Handling: use Table 6 as the key requires; record as data issue.\n2. DCR control-region count: Table 6/7 and Fig. 6 note say 608 never-treated regions; Table A19 note says 640. Observations in Table 6 equal (640 + treated) x 25 for all three columns (16,175 = 647x25; 16,875 = 675x25; 17,250 = 690x25), not (608 + treated) x 25. Handling: report Observations as printed; flag. (25 = years 1998-2022 is my inference, not stated in the extract.)\n3. DCR prose: abstract says fatal outcomes \"fall by 35%\" whereas Table 7 5 km fatal ATT -0.354 corresponds to ~-30% (Section 6.1 says ~30%); prose says HIV estimates \"close to zero\" while 5 km HIV ATT is -0.340 (insignificant). Handling: tables used; noted.\n4. RESEA prose gives the weeks-on-UI CI as magnitudes \"[1.75 weeks, 2.24 weeks]\" while Table 2 prints [-2.24, -1.75]. Handling: Table 2 used, sign as printed.\n5. RESEA rendering defect: \"p ¡ 0.001\" in Section 3 text (should be p < 0.001). Not used for any key.\n6. RESEA Tables 2-3 re-set by extract editor (stated in header); values declared verbatim. Table 3 col 1 duplicates Table 2 ATE column.\n\n## Information-availability\nNo input violates the rules; only the two extracts used. README used only for provenance. No exclusions/filters applied (exclusion ledger has a single no-op row).\nEOF\ncat out/exclusions.csv","description":"Write data audit file"}}]}}54{"type":"api-request","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","id":"90135158-a04c-4511-94ea-4f8c54eacb5a","timestamp":"2026-10-30T05:24:43.799Z","version":"2.1.314","querySource":"sdk","shapeHash":"6cadd754b990b914a32307d3fb20fcdbaf3f1a460ab21338160883bccbb38440","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":"8dde9a66-fbf2-43d8-a1d6-ca4679bc5312","keep":14,"tail":["253a6fa2f180f2f4f5164a16f19c4793eb92601e261330845480a8f185b5d8f1","cbc502f19a003b49dbc91e794e8d49cba190d1caf09e16ce29cd4a76483a694f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"61e75ad61bfe27e7ab926a0c56104d690a3c629a4b75d97fcc64f4e70f4941cc"}55{"parentUuid":"ca87ea5d-594b-499d-aa7c-c1e9df077a69","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs6vVakrxgZ23CTTuFo","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1401,"cache_read_input_tokens":26393,"output_tokens":6821,"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":1401},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs6vDDPX4PXT3iU7RqN","requestRef":"90135158-a04c-4511-94ea-4f8c54eacb5a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8bac6e0c-31f9-4540-8f6d-ca3821ecb44f","timestamp":"2026-10-30T05:24:45.786Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"8bac6e0c-31f9-4540-8f6d-ca3821ecb44f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs6vVakrxgZ23CTTuFo","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01F4P33iVNKWGgATFKkC1TLL","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts, derive CI/SE, write out/results.json.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/. No randomness.\"\"\"\nimport json, re, os\nfrom decimal import Decimal, ROUND_HALF_UP\n\nROOT = os.getcwd()\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\n\ndef lines_of(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\n\ndef num(tok):\n    \"\"\"Printed token -> number preserving printed value (strip stars, %, $, commas, parentheses).\"\"\"\n    t = tok.strip().replace(\"$\", \"\").replace(\",\", \"\").strip(\"()\").rstrip(\"*\").strip()\n    t = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", t)\n    v = float(t)\n    return int(v) if re.fullmatch(r\"-?\\d+\", t) else v\n\n\ndef stars(tok):\n    return len(tok.strip()) - len(tok.strip().rstrip(\"*\"))\n\n\ndef show(tag, ln_no, text):\n    print(f\"[{tag}] line {ln_no}: {text}\")\n\n\n# ---------------- DCR source ----------------\nD = lines_of(DCR_F)\n\n\ndef table_block(L, title_prefix, end_prefix=\"Note:\"):\n    s = next(i for i, l in enumerate(L) if l.startswith(title_prefix))\n    e = next(i for i in range(s + 1, len(L)) if L[i].startswith(end_prefix))\n    return s, e\n\n\ndef dcr_table(title_prefix):\n    s, e = table_block(D, title_prefix)\n    nonempty = [(i, D[i]) for i in range(s + 1, e) if D[i].strip()]\n    hdr_i, hdr = nonempty[0]\n    cols = [c.strip() for c in hdr.split(\"|\")][1:]\n    rows = {}\n    k = 1\n    while k < len(nonempty):\n        i, l = nonempty[k]\n        cells = [c.strip() for c in l.split(\"|\")]\n        label, vals = cells[0], cells[1:]\n        se = None\n        if k + 1 < len(nonempty) and nonempty[k + 1][1].lstrip().startswith(\"|\") and \"(\" in nonempty[k + 1][1]:\n            j, sl = nonempty[k + 1]\n            se = ([c.strip() for c in sl.split(\"|\")][1:], j, sl)\n            k += 1\n        rows[label] = (vals, i, l, se)\n        k += 1\n    return cols, rows\n\n\ndef dcr_cell(title, row, col):\n    cols, rows = dcr_table(title)\n    vals, i, l, se = rows[row]\n    c = cols.index(col)\n    show(f\"{DCR_ID} {title.split(':')[0]}\", i + 1, l)\n    out = {\"est_tok\": vals[c], \"est\": num(vals[c]), \"stars\": stars(vals[c])}\n    if se is not None:\n        show(f\"{DCR_ID} {title.split(':')[0]} SE\", se[1] + 1, se[2])\n        out[\"se_tok\"] = se[0][c]\n        out[\"se\"] = num(se[0][c])\n    return out\n\n\nT6, T7 = \"Table 6:\", \"Table 7:\"\nr = {}\nloc = {}\n\n\ndef L(src, tab, row, col, extra=\"\"):\n    return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\n\na5 = dcr_cell(T6, \"ATT\", \"5 km\")\na3 = dcr_cell(T6, \"ATT\", \"3 km\")\nob5 = dcr_cell(T6, \"Observations\", \"5 km\")\nnar3 = dcr_cell(T7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(T7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(T7, \"HIV\", \"5 km\")\n\nr[\"dcr_att_5km\"], r[\"dcr_att_5km_se\"] = a5[\"est\"], a5[\"se\"]\nloc[\"dcr_att_5km\"] = L(DCR_ID, 6, \"ATT\", \"5 km\")\nloc[\"dcr_att_5km_se\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" (standard error in parentheses on the line below the coefficient row)\")\nr[\"dcr_att_3km\"], r[\"dcr_att_3km_se\"] = a3[\"est\"], a3[\"se\"]\nloc[\"dcr_att_3km\"] = L(DCR_ID, 6, \"ATT\", \"3 km\")\nloc[\"dcr_att_3km_se\"] = L(DCR_ID, 6, \"ATT\", \"3 km\", \" (standard error in parentheses on the line below the coefficient row)\")\nr[\"dcr_obs_5km\"] = int(ob5[\"est\"])\nloc[\"dcr_obs_5km\"] = L(DCR_ID, 6, \"Observations\", \"5 km\")\nr[\"dcr_narcotics_att_3km\"], r[\"dcr_narcotics_att_3km_se\"] = nar3[\"est\"], nar3[\"se\"]\nloc[\"dcr_narcotics_att_3km\"] = L(DCR_ID, 7, \"Narcotics\", \"3 km\")\nloc[\"dcr_narcotics_att_3km_se\"] = L(DCR_ID, 7, \"Narcotics\", \"3 km\", \" (standard error in parentheses on the line below)\")\nr[\"dcr_fatal_att_1km\"], r[\"dcr_fatal_att_1km_se\"] = fat1[\"est\"], fat1[\"se\"]\nloc[\"dcr_fatal_att_1km\"] = L(DCR_ID, 7, \"Drug-related (fatal)\", \"1 km\")\nloc[\"dcr_fatal_att_1km_se\"] = L(DCR_ID, 7, \"Drug-related (fatal)\", \"1 km\", \" (standard error in parentheses on the line below)\")\nr[\"dcr_hiv_att_5km\"], r[\"dcr_hiv_att_5km_se\"] = hiv5[\"est\"], hiv5[\"se\"]\nloc[\"dcr_hiv_att_5km\"] = L(DCR_ID, 7, \"HIV\", \"5 km\")\nloc[\"dcr_hiv_att_5km_se\"] = L(DCR_ID, 7, \"HIV\", \"5 km\", \" (standard error in parentheses on the line below)\")\n\n# exact p-value: search the extract for any printed p-value (p = / p= followed by digits)\npv_hits = [(i + 1, l) for i, l in enumerate(D) if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\nprint(f\"[{DCR_ID}] lines with an exact printed p-value: {len(pv_hits)}\")\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, the SE in \"\n                                  \"parentheses and significance stars; no exact p-value is printed anywhere in the extract)\")\nr[\"dcr_hiv_att_5km_significant_10pct\"] = hiv5[\"stars\"] >= 1\nprint(f\"[{DCR_ID}] HIV 5 km token '{hiv5['est_tok']}' has {hiv5['stars']} star(s) -> significant at 10%: {hiv5['stars'] >= 1}\")\n\n\ndef r3(x):\n    return float(Decimal(repr(x)).quantize(Decimal(\"0.001\"), rounding=ROUND_HALF_UP))\n\n\nlo_raw = a5[\"est\"] - Z * a5[\"se\"]\nhi_raw = a5[\"est\"] + Z * a5[\"se\"]\nr[\"dcr_att_5km_ci_low\"], r[\"dcr_att_5km_ci_high\"] = r3(lo_raw), r3(hi_raw)\nloc[\"dcr_att_5km_ci_low\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" — derived: ATT - 1.96*SE from the printed Table 6 values\")\nloc[\"dcr_att_5km_ci_high\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" — derived: ATT + 1.96*SE from the printed Table 6 values\")\n\n# ---------------- RESEA source ----------------\nR = lines_of(RI_F)\n\n\ndef ri_table(header_start, after_marker):\n    s = next(i for i, l in enumerate(R) if l.startswith(after_marker))\n    h = next(i for i in range(s + 1, len(R)) if R[i].startswith(header_start))\n    cols = [c.strip() for c in R[h].split(\"|\")]\n    rows = {}\n    i = h + 1\n    while i < len(R) and R[i].strip():\n        cells = [c.strip() for c in R[i].split(\"|\")]\n        rows[cells[0]] = (cells, i)\n        i += 1\n    return cols, rows\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,\\.]+)(?:\\s*(?:pp|weeks|wks))?;\\s*p\\s*([=<])\\s*([\\d\\.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})*),\\s*([^\\]]+)\\]$\", cell)\n    # robust split instead: estimate ; p ; CI\n    parts = [p.strip() for p in cell.split(\";\")]\n    est = num(parts[0])\n    ci = re.search(r\"\\[(.*)\\]\", parts[2]).group(1)\n    # CI bounds separated by \", \" (thousands separators have no space)\n    lo, hi = [x.strip() for x in ci.split(\", \")]\n    return {\"est\": est, \"p\": parts[1], \"lo\": num(lo), \"hi\": num(hi), \"ci_tok\": ci}\n\n\ncols2, rows2 = ri_table(\"Outcome | Treatment\", \"[Table 2\")\nATE_COL = \"Average treatment effect estimate\"\nci_ = cols2.index(ATE_COL)\ncc = cols2.index(\"Control\")\n\n\ndef ri_row(name):\n    cells, i = rows2[name]\n    show(f\"{RI_ID} Table 2\", i + 1, R[i])\n    return cells\n\n\nw = ri_row(\"Wage\")\nwe = parse_effect(w[ci_])\nr[\"ri_wage_ate\"], r[\"ri_wage_ate_ci_low\"], r[\"ri_wage_ate_ci_high\"] = we[\"est\"], we[\"lo\"], we[\"hi\"]\nloc[\"ri_wage_ate\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (point estimate)\")\nloc[\"ri_wage_ate_ci_low\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (95% CI lower bound)\")\nloc[\"ri_wage_ate_ci_high\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (95% CI upper bound)\")\nr[\"ri_wage_ate_se_reported\"] = (\"SE\" in w[ci_]) or (\"(\" in w[ci_])\nprint(f\"[{RI_ID}] wage ATE cell contains a standard error: {r['ri_wage_ate_se_reported']}\")\nr[\"ri_wage_ate_se_derived\"] = round((we[\"hi\"] - we[\"lo\"]) / (2 * Z), 2)\nloc[\"ri_wage_ate_se_derived\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" — derived: (CI upper - CI lower)/(2*1.96) from the printed Table 2 95% CI\")\nctrl_tok = w[cc].split(\"(\")[0].strip()\nr[\"ri_control_mean_wage\"] = num(ctrl_tok)\nloc[\"ri_control_mean_wage\"] = L(RI_ID, 2, \"Wage\", \"Control\", \" (mean; SD in parentheses not used)\")\n\nre_ = parse_effect(ri_row(\"Reemployment\")[ci_])\nr[\"ri_reemployment_ate_pp\"] = re_[\"est\"]\nloc[\"ri_reemployment_ate_pp\"] = L(RI_ID, 2, \"Reemployment\", ATE_COL, \" (point estimate, pp)\")\n\nwu = parse_effect(ri_row(\"Weeks on UI\")[ci_])\nr[\"ri_weeks_ui_ate\"], r[\"ri_weeks_ui_ate_ci_low\"], r[\"ri_weeks_ui_ate_ci_high\"] = wu[\"est\"], wu[\"lo\"], wu[\"hi\"]\nloc[\"ri_weeks_ui_ate\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (point estimate)\")\nloc[\"ri_weeks_ui_ate_ci_low\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (95% CI lower bound)\")\nloc[\"ri_weeks_ui_ate_ci_high\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (95% CI upper bound)\")\n\n# Cross-check with Table 3 (duplicate of Table 2 ATE column) — informational only\ncols3, rows3 = ri_table(\"Outcome | Difference in means\", \"[Table 3\")\nt3_w = parse_effect(rows3[\"Wage\"][0][cols3.index(\"Difference in means\")])\nt3_match = (t3_w[\"est\"], t3_w[\"lo\"], t3_w[\"hi\"]) == (we[\"est\"], we[\"lo\"], we[\"hi\"])\nprint(f\"[{RI_ID}] Table 3 'Difference in means' Wage equals Table 2 ATE Wage: {t3_match}\")\n\n# ---------------- Cross-table checks feeding data_issues ----------------\ncols_a19, rows_a19 = dcr_table(\"Table A19:\")\na19 = rows_a19[\"ATT\"]\na19_se = num(a19[3][0][cols_a19.index(\"CS-DR\")])\na19_est = num(a19[0][cols_a19.index(\"CS-DR\")])\nprint(f\"[{DCR_ID}] Table A19 CS-DR 5 km: ATT {a19_est}, SE {a19_se}; Table 6 5 km: ATT {a5['est']}, SE {a5['se']}\")\n_, rows6 = dcr_table(T6)\nobs = [num(x) for x in rows6[\"Observations\"][0]]\ntreated = [num(x) for x in rows6[\"Treated regions\"][0]]\nctrl6 = [num(x) for x in rows6[\"Control regions\"][0]]\nimplied_ctrl = [o / 25 - t for o, t in zip(obs, treated)]\nprint(f\"[{DCR_ID}] Observations/25 - treated = {implied_ctrl}; printed control regions = {ctrl6}\")\n\ndata_issues = [\n    {\"issue\": f\"{DCR_ID}: the 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but SE ({a19_se}) in Table A19 (same estimator, buffer, window, and 17,250 observations).\",\n     \"handling\": \"Used the Table 6 value (0.210) as the key definition names Table 6; the derived 95% CI uses 0.210. Flagged as an internal inconsistency of the source.\"},\n    {\"issue\": f\"{DCR_ID}: control-region count is 608 in Tables 6/7 and the figure notes but 640 in the Table A19 note; the Table 6 Observations (16,175 / 16,875 / 17,250) equal (640 + treated regions) x 25 in every column, not (608 + treated) x 25.\",\n     \"handling\": \"Observations reported as printed (17250); inconsistency flagged only (the 25-period panel length is an inference, not stated in the extract).\"},\n    {\"issue\": f\"{DCR_ID}: prose differs from tables — the abstract says fatal outcomes fall by 35% while the Table 7 5 km fatal ATT -0.354 corresponds to about -30% (Section 6.1 says ~30%); the text calls HIV estimates 'close to zero' while the 5 km HIV ATT is -0.340 (not starred).\",\n     \"handling\": \"All keys taken from the tables named in TASK.md; prose not used.\"},\n    {\"issue\": f\"{RI_ID}: Section 3 text reports the weeks-on-UI ATE CI as magnitudes '[1.75 weeks, 2.24 weeks]' whereas Table 2 prints [-2.24, -1.75].\",\n     \"handling\": \"Used Table 2 values with printed signs (lower bound -2.24 first).\"},\n    {\"issue\": f\"{RI_ID}: rendering defect 'p ¡ 0.001' (for p < 0.001) in the Section 3 short-term weeks-on-UI sentence; Tables 2-3 were re-set by the extract editor into one row per outcome.\",\n     \"handling\": \"Defective text not used; re-set table values used as declared verbatim; Table 3 column 1 Wage ATE cross-checked equal to Table 2.\"},\n]\n\nn_input_rows = {\"README.md\": wc_l(README_F), DCR_ID + \".txt\": wc_l(DCR_F), RI_ID + \".txt\": wc_l(RI_F)}\n\nresults = {**r,\n           \"locators\": loc,\n           \"derivation_notes\": {\n               \"dcr_att_5km_ci\": (\"Normal-approximation (Wald) 95% CI: ATT +/- 1.96 x SE using printed Table 6 values ATT = -0.431, SE = 0.210 \"\n                                  f\"(unrounded {lo_raw:.4f}, {hi_raw:.4f}; reported rounded to 3 decimals as in the table). Assumes asymptotic normality \"\n                                  \"of the CS-DR estimator and the clustered SE; not printed by the source. Using Table A19's SE 0.218 instead would give a wider interval.\"),\n               \"ri_wage_ate_se\": (\"SE = (upper - lower)/(2 x 1.96) from the printed Table 2 95% CI [166, 2,141] = 1975/3.92 \"\n                                  f\"(unrounded {(we['hi'] - we['lo']) / (2 * Z):.4f}; reported to 2 decimals). Assumes the printed CI is a symmetric normal-approximation \"\n                                  \"Wald interval (z = 1.96; with N = 23,549 a t critical value is practically identical); the CI bounds are rounded to whole dollars, so the \"\n                                  \"derived SE is accurate only to about +/-0.3 USD. Source prints no SE.\")},\n           \"task_id\": \"F4-conf-01\",\n           \"feasibility\": \"complete\",\n           \"feasibility_reason\": None,\n           \"data_issues\": data_issues,\n           \"n_input_rows\": n_input_rows,\n           \"n_analysed\": 22,\n           \"random_seed\": None,\n           }\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)\nprint(\"wrote out/results.json\")\nfor k in r:\n    print(f\"  {k} = {r[k]}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1401,"cache_read_input_tokens":26393,"output_tokens":6821,"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":1401},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs6vDDPX4PXT3iU7RqN","requestRef":"90135158-a04c-4511-94ea-4f8c54eacb5a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0ad47292-8805-4703-b1ed-0120708d2ab4","timestamp":"2026-10-30T05:25:36.063Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"0ad47292-8805-4703-b1ed-0120708d2ab4","isSidechain":false,"promptId":"af83ad24-a245-4ceb-92cd-35a6d1a3a3e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F4P33iVNKWGgATFKkC1TLL","type":"tool_result","content":"[arxiv-2609.27732v1 Table 6] line 36: ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 Table 6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 Table 6] line 36: ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 Table 6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 Table 6] line 46: Observations | 16,175 | 16,875 | 17,250\n[arxiv-2609.27732v1 Table 7] line 82: Narcotics | 1.186*** | 0.549*** | 0.390**\n[arxiv-2609.27732v1 Table 7 SE] line 84: | (0.288) | (0.187) | (0.165)\n[arxiv-2609.27732v1 Table 7] line 86: Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2609.27732v1 Table 7 SE] line 88: | (0.137) | (0.215) | (0.178)\n[arxiv-2609.27732v1 Table 7] line 78: HIV | 0.063 | -0.239 | -0.340\n[arxiv-2609.27732v1 Table 7 SE] line 80: | (0.801) | (0.337) | (0.294)\n[arxiv-2609.27732v1] lines with an exact printed p-value: 0\n[arxiv-2609.27732v1] HIV 5 km token '-0.340' has 0 star(s) -> significant at 10%: False\n[arxiv-2606.14621v1 Table 2] line 28: 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[arxiv-2606.14621v1] wage ATE cell contains a standard error: False\n[arxiv-2606.14621v1 Table 2] line 29: 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[arxiv-2606.14621v1 Table 2] line 30: 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[arxiv-2606.14621v1] Table 3 'Difference in means' Wage equals Table 2 ATE Wage: True\n[arxiv-2609.27732v1] Table A19 CS-DR 5 km: ATT -0.431, SE 0.218; Table 6 5 km: ATT -0.431, SE 0.21\n[arxiv-2609.27732v1] Observations/25 - treated = [640.0, 640.0, 640.0]; printed control regions = [608, 608, 608]\nwrote out/results.json\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 = None\n  dcr_hiv_att_5km_significant_10pct = False\n  dcr_att_5km_ci_low = -0.843\n  dcr_att_5km_ci_high = -0.019\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 = False\n  ri_wage_ate_se_derived = 503.83\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","is_error":false}]},"uuid":"a906036f-5222-42f1-b659-24fa5d86e814","timestamp":"2026-10-30T05:25:36.115Z","toolUseResult":{"stdout":"[arxiv-2609.27732v1 Table 6] line 36: ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 Table 6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 Table 6] line 36: ATT | -0.399 | -0.283 | -0.431**\n[arxiv-2609.27732v1 Table 6 SE] line 38: | (0.412) | (0.279) | (0.210)\n[arxiv-2609.27732v1 Table 6] line 46: Observations | 16,175 | 16,875 | 17,250\n[arxiv-2609.27732v1 Table 7] line 82: Narcotics | 1.186*** | 0.549*** | 0.390**\n[arxiv-2609.27732v1 Table 7 SE] line 84: | (0.288) | (0.187) | (0.165)\n[arxiv-2609.27732v1 Table 7] line 86: Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n[arxiv-2609.27732v1 Table 7 SE] line 88: | (0.137) | (0.215) | (0.178)\n[arxiv-2609.27732v1 Table 7] line 78: HIV | 0.063 | -0.239 | -0.340\n[arxiv-2609.27732v1 Table 7 SE] line 80: | (0.801) | (0.337) | (0.294)\n[arxiv-2609.27732v1] lines with an exact printed p-value: 0\n[arxiv-2609.27732v1] HIV 5 km token '-0.340' has 0 star(s) -> significant at 10%: False\n[arxiv-2606.14621v1 Table 2] line 28: 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[arxiv-2606.14621v1] wage ATE cell contains a standard error: False\n[arxiv-2606.14621v1 Table 2] line 29: 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[arxiv-2606.14621v1 Table 2] line 30: 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[arxiv-2606.14621v1] Table 3 'Difference in means' Wage equals Table 2 ATE Wage: True\n[arxiv-2609.27732v1] Table A19 CS-DR 5 km: ATT -0.431, SE 0.218; Table 6 5 km: ATT -0.431, SE 0.21\n[arxiv-2609.27732v1] Observations/25 - treated = [640.0, 640.0, 640.0]; printed control regions = [608, 608, 608]\nwrote out/results.json\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 = None\n  dcr_hiv_att_5km_significant_10pct = False\n  dcr_att_5km_ci_low = -0.843\n  dcr_att_5km_ci_high = -0.019\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 = False\n  ri_wage_ate_se_derived = 503.83\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0ad47292-8805-4703-b1ed-0120708d2ab4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"dc17ce3a548eea30e9fa45b00454123a15c1cb1502c301bc0f8441391dc7534f","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_01F4P33iVNKWGgATFKkC1TLL","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts, derive CI/SE, write out/results.json.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/. No randomness.\"\"\"\nimport json, re, os\nfrom decimal import Decimal, ROUND_HALF_UP\n\nROOT = os.getcwd()\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\n\ndef lines_of(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\n\ndef num(tok):\n    \"\"\"Printed token -> number preserving printed value (strip stars, %, $, commas, parentheses).\"\"\"\n    t = tok.strip().replace(\"$\", \"\").replace(\",\", \"\").strip(\"()\").rstrip(\"*\").strip()\n    t = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", t)\n    v = float(t)\n    return int(v) if re.fullmatch(r\"-?\\d+\", t) else v\n\n\ndef stars(tok):\n    return len(tok.strip()) - len(tok.strip().rstrip(\"*\"))\n\n\ndef show(tag, ln_no, text):\n    print(f\"[{tag}] line {ln_no}: {text}\")\n\n\n# ---------------- DCR source ----------------\nD = lines_of(DCR_F)\n\n\ndef table_block(L, title_prefix, end_prefix=\"Note:\"):\n    s = next(i for i, l in enumerate(L) if l.startswith(title_prefix))\n    e = next(i for i in range(s + 1, len(L)) if L[i].startswith(end_prefix))\n    return s, e\n\n\ndef dcr_table(title_prefix):\n    s, e = table_block(D, title_prefix)\n    nonempty = [(i, D[i]) for i in range(s + 1, e) if D[i].strip()]\n    hdr_i, hdr = nonempty[0]\n    cols = [c.strip() for c in hdr.split(\"|\")][1:]\n    rows = {}\n    k = 1\n    while k < len(nonempty):\n        i, l = nonempty[k]\n        cells = [c.strip() for c in l.split(\"|\")]\n        label, vals = cells[0], cells[1:]\n        se = None\n        if k + 1 < len(nonempty) and nonempty[k + 1][1].lstrip().startswith(\"|\") and \"(\" in nonempty[k + 1][1]:\n            j, sl = nonempty[k + 1]\n            se = ([c.strip() for c in sl.split(\"|\")][1:], j, sl)\n            k += 1\n        rows[label] = (vals, i, l, se)\n        k += 1\n    return cols, rows\n\n\ndef dcr_cell(title, row, col):\n    cols, rows = dcr_table(title)\n    vals, i, l, se = rows[row]\n    c = cols.index(col)\n    show(f\"{DCR_ID} {title.split(':')[0]}\", i + 1, l)\n    out = {\"est_tok\": vals[c], \"est\": num(vals[c]), \"stars\": stars(vals[c])}\n    if se is not None:\n        show(f\"{DCR_ID} {title.split(':')[0]} SE\", se[1] + 1, se[2])\n        out[\"se_tok\"] = se[0][c]\n        out[\"se\"] = num(se[0][c])\n    return out\n\n\nT6, T7 = \"Table 6:\", \"Table 7:\"\nr = {}\nloc = {}\n\n\ndef L(src, tab, row, col, extra=\"\"):\n    return f'{src} | Table {tab} | row \"{row}\" | column \"{col}\"' + extra\n\n\na5 = dcr_cell(T6, \"ATT\", \"5 km\")\na3 = dcr_cell(T6, \"ATT\", \"3 km\")\nob5 = dcr_cell(T6, \"Observations\", \"5 km\")\nnar3 = dcr_cell(T7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(T7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(T7, \"HIV\", \"5 km\")\n\nr[\"dcr_att_5km\"], r[\"dcr_att_5km_se\"] = a5[\"est\"], a5[\"se\"]\nloc[\"dcr_att_5km\"] = L(DCR_ID, 6, \"ATT\", \"5 km\")\nloc[\"dcr_att_5km_se\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" (standard error in parentheses on the line below the coefficient row)\")\nr[\"dcr_att_3km\"], r[\"dcr_att_3km_se\"] = a3[\"est\"], a3[\"se\"]\nloc[\"dcr_att_3km\"] = L(DCR_ID, 6, \"ATT\", \"3 km\")\nloc[\"dcr_att_3km_se\"] = L(DCR_ID, 6, \"ATT\", \"3 km\", \" (standard error in parentheses on the line below the coefficient row)\")\nr[\"dcr_obs_5km\"] = int(ob5[\"est\"])\nloc[\"dcr_obs_5km\"] = L(DCR_ID, 6, \"Observations\", \"5 km\")\nr[\"dcr_narcotics_att_3km\"], r[\"dcr_narcotics_att_3km_se\"] = nar3[\"est\"], nar3[\"se\"]\nloc[\"dcr_narcotics_att_3km\"] = L(DCR_ID, 7, \"Narcotics\", \"3 km\")\nloc[\"dcr_narcotics_att_3km_se\"] = L(DCR_ID, 7, \"Narcotics\", \"3 km\", \" (standard error in parentheses on the line below)\")\nr[\"dcr_fatal_att_1km\"], r[\"dcr_fatal_att_1km_se\"] = fat1[\"est\"], fat1[\"se\"]\nloc[\"dcr_fatal_att_1km\"] = L(DCR_ID, 7, \"Drug-related (fatal)\", \"1 km\")\nloc[\"dcr_fatal_att_1km_se\"] = L(DCR_ID, 7, \"Drug-related (fatal)\", \"1 km\", \" (standard error in parentheses on the line below)\")\nr[\"dcr_hiv_att_5km\"], r[\"dcr_hiv_att_5km_se\"] = hiv5[\"est\"], hiv5[\"se\"]\nloc[\"dcr_hiv_att_5km\"] = L(DCR_ID, 7, \"HIV\", \"5 km\")\nloc[\"dcr_hiv_att_5km_se\"] = L(DCR_ID, 7, \"HIV\", \"5 km\", \" (standard error in parentheses on the line below)\")\n\n# exact p-value: search the extract for any printed p-value (p = / p= followed by digits)\npv_hits = [(i + 1, l) for i, l in enumerate(D) if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\nprint(f\"[{DCR_ID}] lines with an exact printed p-value: {len(pv_hits)}\")\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, the SE in \"\n                                  \"parentheses and significance stars; no exact p-value is printed anywhere in the extract)\")\nr[\"dcr_hiv_att_5km_significant_10pct\"] = hiv5[\"stars\"] >= 1\nprint(f\"[{DCR_ID}] HIV 5 km token '{hiv5['est_tok']}' has {hiv5['stars']} star(s) -> significant at 10%: {hiv5['stars'] >= 1}\")\n\n\ndef r3(x):\n    return float(Decimal(repr(x)).quantize(Decimal(\"0.001\"), rounding=ROUND_HALF_UP))\n\n\nlo_raw = a5[\"est\"] - Z * a5[\"se\"]\nhi_raw = a5[\"est\"] + Z * a5[\"se\"]\nr[\"dcr_att_5km_ci_low\"], r[\"dcr_att_5km_ci_high\"] = r3(lo_raw), r3(hi_raw)\nloc[\"dcr_att_5km_ci_low\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" — derived: ATT - 1.96*SE from the printed Table 6 values\")\nloc[\"dcr_att_5km_ci_high\"] = L(DCR_ID, 6, \"ATT\", \"5 km\", \" — derived: ATT + 1.96*SE from the printed Table 6 values\")\n\n# ---------------- RESEA source ----------------\nR = lines_of(RI_F)\n\n\ndef ri_table(header_start, after_marker):\n    s = next(i for i, l in enumerate(R) if l.startswith(after_marker))\n    h = next(i for i in range(s + 1, len(R)) if R[i].startswith(header_start))\n    cols = [c.strip() for c in R[h].split(\"|\")]\n    rows = {}\n    i = h + 1\n    while i < len(R) and R[i].strip():\n        cells = [c.strip() for c in R[i].split(\"|\")]\n        rows[cells[0]] = (cells, i)\n        i += 1\n    return cols, rows\n\n\ndef parse_effect(cell):\n    m = re.match(r\"^(-?\\$?-?[\\d,\\.]+)(?:\\s*(?:pp|weeks|wks))?;\\s*p\\s*([=<])\\s*([\\d\\.]+);\\s*95% CI:\\s*\\[([^,\\]]+(?:,\\d{3})*),\\s*([^\\]]+)\\]$\", cell)\n    # robust split instead: estimate ; p ; CI\n    parts = [p.strip() for p in cell.split(\";\")]\n    est = num(parts[0])\n    ci = re.search(r\"\\[(.*)\\]\", parts[2]).group(1)\n    # CI bounds separated by \", \" (thousands separators have no space)\n    lo, hi = [x.strip() for x in ci.split(\", \")]\n    return {\"est\": est, \"p\": parts[1], \"lo\": num(lo), \"hi\": num(hi), \"ci_tok\": ci}\n\n\ncols2, rows2 = ri_table(\"Outcome | Treatment\", \"[Table 2\")\nATE_COL = \"Average treatment effect estimate\"\nci_ = cols2.index(ATE_COL)\ncc = cols2.index(\"Control\")\n\n\ndef ri_row(name):\n    cells, i = rows2[name]\n    show(f\"{RI_ID} Table 2\", i + 1, R[i])\n    return cells\n\n\nw = ri_row(\"Wage\")\nwe = parse_effect(w[ci_])\nr[\"ri_wage_ate\"], r[\"ri_wage_ate_ci_low\"], r[\"ri_wage_ate_ci_high\"] = we[\"est\"], we[\"lo\"], we[\"hi\"]\nloc[\"ri_wage_ate\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (point estimate)\")\nloc[\"ri_wage_ate_ci_low\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (95% CI lower bound)\")\nloc[\"ri_wage_ate_ci_high\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" (95% CI upper bound)\")\nr[\"ri_wage_ate_se_reported\"] = (\"SE\" in w[ci_]) or (\"(\" in w[ci_])\nprint(f\"[{RI_ID}] wage ATE cell contains a standard error: {r['ri_wage_ate_se_reported']}\")\nr[\"ri_wage_ate_se_derived\"] = round((we[\"hi\"] - we[\"lo\"]) / (2 * Z), 2)\nloc[\"ri_wage_ate_se_derived\"] = L(RI_ID, 2, \"Wage\", ATE_COL, \" — derived: (CI upper - CI lower)/(2*1.96) from the printed Table 2 95% CI\")\nctrl_tok = w[cc].split(\"(\")[0].strip()\nr[\"ri_control_mean_wage\"] = num(ctrl_tok)\nloc[\"ri_control_mean_wage\"] = L(RI_ID, 2, \"Wage\", \"Control\", \" (mean; SD in parentheses not used)\")\n\nre_ = parse_effect(ri_row(\"Reemployment\")[ci_])\nr[\"ri_reemployment_ate_pp\"] = re_[\"est\"]\nloc[\"ri_reemployment_ate_pp\"] = L(RI_ID, 2, \"Reemployment\", ATE_COL, \" (point estimate, pp)\")\n\nwu = parse_effect(ri_row(\"Weeks on UI\")[ci_])\nr[\"ri_weeks_ui_ate\"], r[\"ri_weeks_ui_ate_ci_low\"], r[\"ri_weeks_ui_ate_ci_high\"] = wu[\"est\"], wu[\"lo\"], wu[\"hi\"]\nloc[\"ri_weeks_ui_ate\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (point estimate)\")\nloc[\"ri_weeks_ui_ate_ci_low\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (95% CI lower bound)\")\nloc[\"ri_weeks_ui_ate_ci_high\"] = L(RI_ID, 2, \"Weeks on UI\", ATE_COL, \" (95% CI upper bound)\")\n\n# Cross-check with Table 3 (duplicate of Table 2 ATE column) — informational only\ncols3, rows3 = ri_table(\"Outcome | Difference in means\", \"[Table 3\")\nt3_w = parse_effect(rows3[\"Wage\"][0][cols3.index(\"Difference in means\")])\nt3_match = (t3_w[\"est\"], t3_w[\"lo\"], t3_w[\"hi\"]) == (we[\"est\"], we[\"lo\"], we[\"hi\"])\nprint(f\"[{RI_ID}] Table 3 'Difference in means' Wage equals Table 2 ATE Wage: {t3_match}\")\n\n# ---------------- Cross-table checks feeding data_issues ----------------\ncols_a19, rows_a19 = dcr_table(\"Table A19:\")\na19 = rows_a19[\"ATT\"]\na19_se = num(a19[3][0][cols_a19.index(\"CS-DR\")])\na19_est = num(a19[0][cols_a19.index(\"CS-DR\")])\nprint(f\"[{DCR_ID}] Table A19 CS-DR 5 km: ATT {a19_est}, SE {a19_se}; Table 6 5 km: ATT {a5['est']}, SE {a5['se']}\")\n_, rows6 = dcr_table(T6)\nobs = [num(x) for x in rows6[\"Observations\"][0]]\ntreated = [num(x) for x in rows6[\"Treated regions\"][0]]\nctrl6 = [num(x) for x in rows6[\"Control regions\"][0]]\nimplied_ctrl = [o / 25 - t for o, t in zip(obs, treated)]\nprint(f\"[{DCR_ID}] Observations/25 - treated = {implied_ctrl}; printed control regions = {ctrl6}\")\n\ndata_issues = [\n    {\"issue\": f\"{DCR_ID}: the 5 km CS-DR ATT -0.431 has SE (0.210) in Table 6 but SE ({a19_se}) in Table A19 (same estimator, buffer, window, and 17,250 observations).\",\n     \"handling\": \"Used the Table 6 value (0.210) as the key definition names Table 6; the derived 95% CI uses 0.210. Flagged as an internal inconsistency of the source.\"},\n    {\"issue\": f\"{DCR_ID}: control-region count is 608 in Tables 6/7 and the figure notes but 640 in the Table A19 note; the Table 6 Observations (16,175 / 16,875 / 17,250) equal (640 + treated regions) x 25 in every column, not (608 + treated) x 25.\",\n     \"handling\": \"Observations reported as printed (17250); inconsistency flagged only (the 25-period panel length is an inference, not stated in the extract).\"},\n    {\"issue\": f\"{DCR_ID}: prose differs from tables — the abstract says fatal outcomes fall by 35% while the Table 7 5 km fatal ATT -0.354 corresponds to about -30% (Section 6.1 says ~30%); the text calls HIV estimates 'close to zero' while the 5 km HIV ATT is -0.340 (not starred).\",\n     \"handling\": \"All keys taken from the tables named in TASK.md; prose not used.\"},\n    {\"issue\": f\"{RI_ID}: Section 3 text reports the weeks-on-UI ATE CI as magnitudes '[1.75 weeks, 2.24 weeks]' whereas Table 2 prints [-2.24, -1.75].\",\n     \"handling\": \"Used Table 2 values with printed signs (lower bound -2.24 first).\"},\n    {\"issue\": f\"{RI_ID}: rendering defect 'p ¡ 0.001' (for p < 0.001) in the Section 3 short-term weeks-on-UI sentence; Tables 2-3 were re-set by the extract editor into one row per outcome.\",\n     \"handling\": \"Defective text not used; re-set table values used as declared verbatim; Table 3 column 1 Wage ATE cross-checked equal to Table 2.\"},\n]\n\nn_input_rows = {\"README.md\": wc_l(README_F), DCR_ID + \".txt\": wc_l(DCR_F), RI_ID + \".txt\": wc_l(RI_F)}\n\nresults = {**r,\n           \"locators\": loc,\n           \"derivation_notes\": {\n               \"dcr_att_5km_ci\": (\"Normal-approximation (Wald) 95% CI: ATT +/- 1.96 x SE using printed Table 6 values ATT = -0.431, SE = 0.210 \"\n                                  f\"(unrounded {lo_raw:.4f}, {hi_raw:.4f}; reported rounded to 3 decimals as in the table). Assumes asymptotic normality \"\n                                  \"of the CS-DR estimator and the clustered SE; not printed by the source. Using Table A19's SE 0.218 instead would give a wider interval.\"),\n               \"ri_wage_ate_se\": (\"SE = (upper - lower)/(2 x 1.96) from the printed Table 2 95% CI [166, 2,141] = 1975/3.92 \"\n                                  f\"(unrounded {(we['hi'] - we['lo']) / (2 * Z):.4f}; reported to 2 decimals). Assumes the printed CI is a symmetric normal-approximation \"\n                                  \"Wald interval (z = 1.96; with N = 23,549 a t critical value is practically identical); the CI bounds are rounded to whole dollars, so the \"\n                                  \"derived SE is accurate only to about +/-0.3 USD. Source prints no SE.\")},\n           \"task_id\": \"F4-conf-01\",\n           \"feasibility\": \"complete\",\n           \"feasibility_reason\": None,\n           \"data_issues\": data_issues,\n           \"n_input_rows\": n_input_rows,\n           \"n_analysed\": 22,\n           \"random_seed\": None,\n           }\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)\nprint(\"wrote out/results.json\")\nfor k in r:\n    print(f\"  {k} = {r[k]}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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- <<'EOF'\nimport re\np='out/analysis.py'; s=open(p).read()\ns=re.sub(r'    m = re\\.match\\(.*\\n    # robust split instead: estimate ; p ; CI\\n','',s)\nopen(p,'w').write(s)\nEOF\ngrep -n \"re.match\" out/analysis.py; cp 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'))\nchk=[]\nchk.append((\"CI contains estimate\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nchk.append((\"CI symmetric about est (±0.001)\", abs((r['dcr_att_5km_ci_low']+r['dcr_att_5km_ci_high'])/2-r['dcr_att_5km'])<=0.001))\nchk.append((\"wage CI contains est\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nchk.append((\"weeks CI ordered & contains est\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nchk.append((\"SEs positive\", all(r[k]>0 for k in r if k.endswith('_se') or k=='ri_wage_ate_se_derived')))\nfrom math import erf,sqrt\npz=lambda z:2*(1-0.5*(1+erf(abs(z)/sqrt(2))))\nchk.append((\"wage p implied by derived SE ~0.022\", round(pz(r['ri_wage_ate']/r['ri_wage_ate_se_derived']),3)==0.022))\nchk.append((\"5km ATT stars ** consistent: 0.01<p<0.05\", 0.01<pz(r['dcr_att_5km']/r['dcr_att_5km_se'])<0.05))\nchk.append((\"HIV 5km unstarred consistent: p>0.10\", pz(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])>0.10))\nchk.append((\"narcotics 3km *** consistent: p<0.01\", pz(r['dcr_narcotics_att_3km']/r['dcr_narcotics_att_3km_se'])<0.01))\nchk.append((\"fatal 1km *** consistent\", pz(r['dcr_fatal_att_1km']/r['dcr_fatal_att_1km_se'])<0.01))\nchk.append((\"derived 95% CI excludes 0 consistent with p<0.05\", r['dcr_att_5km_ci_high']<0))\nchk.append((\"locators for all non-boolean keys\", all(k in r['locators'] for k in r if k.startswith(('dcr_','ri_')) and not isinstance(r[k],bool))))\nchk.append((\"n_input_rows matches wc -l (10,134,58)\", list(r['n_input_rows'].values())==[10,134,58]))\nchk.append((\"exclusions: 22-0=22=n_analysed\", r['n_analysed']==22))\nfor c in chk: print(c)\nEOF","description":"Rerun for reproducibility and run consistency 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contains estimate', True)\n('CI symmetric about est (±0.001)', True)\n('wage CI contains est', True)\n('weeks CI ordered & contains est', True)\n('SEs positive', True)\n('wage p implied by derived SE ~0.022', True)\n('5km ATT stars ** consistent: 0.01<p<0.05', True)\n('HIV 5km unstarred consistent: p>0.10', True)\n('narcotics 3km *** consistent: p<0.01', True)\n('fatal 1km *** consistent', True)\n('derived 95% CI excludes 0 consistent with p<0.05', True)\n('locators for all non-boolean keys', True)\n('n_input_rows matches wc -l (10,134,58)', True)\n('exclusions: 22-0=22=n_analysed', True)","is_error":false}]},"uuid":"1063a400-d614-4f40-8523-4bf57df3431b","timestamp":"2026-10-30T05:25:49.369Z","toolUseResult":{"stdout":"IDENTICAL\n('CI contains estimate', True)\n('CI symmetric about est (±0.001)', True)\n('wage CI contains est', True)\n('weeks CI ordered & contains est', True)\n('SEs positive', True)\n('wage p implied by derived SE ~0.022', True)\n('5km ATT stars ** consistent: 0.01<p<0.05', True)\n('HIV 5km unstarred consistent: p>0.10', True)\n('narcotics 3km *** consistent: p<0.01', True)\n('fatal 1km *** consistent', True)\n('derived 95% CI excludes 0 consistent with p<0.05', True)\n('locators for all non-boolean keys', True)\n('n_input_rows matches wc -l (10,134,58)', True)\n('exclusions: 22-0=22=n_analysed', True)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"756b3e57-e2fe-49b2-9c71-6a170814fbf3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}65{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"87fd565197b16a5a89e8fb4c0d2856491825db9195221a2272e6e37ef3e1c29b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KRa1RcFjffmMiBiY9evEmA","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport re\np='out/analysis.py'; s=open(p).read()\ns=re.sub(r'    m = re\\.match\\(.*\\n    # robust split instead: estimate ; p ; CI\\n','',s)\nopen(p,'w').write(s)\nEOF\ngrep -n \"re.match\" out/analysis.py; cp 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'))\nchk=[]\nchk.append((\"CI contains estimate\", r['dcr_att_5km_ci_low']<r['dcr_att_5km']<r['dcr_att_5km_ci_high']))\nchk.append((\"CI symmetric about est (±0.001)\", abs((r['dcr_att_5km_ci_low']+r['dcr_att_5km_ci_high'])/2-r['dcr_att_5km'])<=0.001))\nchk.append((\"wage CI contains est\", r['ri_wage_ate_ci_low']<r['ri_wage_ate']<r['ri_wage_ate_ci_high']))\nchk.append((\"weeks CI ordered & contains est\", r['ri_weeks_ui_ate_ci_low']<r['ri_weeks_ui_ate']<r['ri_weeks_ui_ate_ci_high']))\nchk.append((\"SEs positive\", all(r[k]>0 for k in r if k.endswith('_se') or k=='ri_wage_ate_se_derived')))\nfrom math import erf,sqrt\npz=lambda z:2*(1-0.5*(1+erf(abs(z)/sqrt(2))))\nchk.append((\"wage p implied by derived SE ~0.022\", round(pz(r['ri_wage_ate']/r['ri_wage_ate_se_derived']),3)==0.022))\nchk.append((\"5km ATT stars ** consistent: 0.01<p<0.05\", 0.01<pz(r['dcr_att_5km']/r['dcr_att_5km_se'])<0.05))\nchk.append((\"HIV 5km unstarred consistent: p>0.10\", pz(r['dcr_hiv_att_5km']/r['dcr_hiv_att_5km_se'])>0.10))\nchk.append((\"narcotics 3km *** consistent: p<0.01\", pz(r['dcr_narcotics_att_3km']/r['dcr_narcotics_att_3km_se'])<0.01))\nchk.append((\"fatal 1km *** consistent\", pz(r['dcr_fatal_att_1km']/r['dcr_fatal_att_1km_se'])<0.01))\nchk.append((\"derived 95% CI excludes 0 consistent with p<0.05\", r['dcr_att_5km_ci_high']<0))\nchk.append((\"locators for all non-boolean keys\", all(k in r['locators'] for k in r if k.startswith(('dcr_','ri_')) and not isinstance(r[k],bool))))\nchk.append((\"n_input_rows matches wc -l (10,134,58)\", list(r['n_input_rows'].values())==[10,134,58]))\nchk.append((\"exclusions: 22-0=22=n_analysed\", r['n_analysed']==22))\nfor c in chk: print(c)\nEOF","description":"Rerun for reproducibility and run consistency checks"}}]}}66{"type":"api-request","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","id":"820b6845-c468-4a91-abb2-1d40ff9c8806","timestamp":"2026-10-30T05:25:49.374Z","version":"2.1.314","querySource":"sdk","shapeHash":"6cadd754b990b914a32307d3fb20fcdbaf3f1a460ab21338160883bccbb38440","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":"30350f06-5cd2-4f8c-9202-265ef89d6a67","keep":18,"tail":["87fd565197b16a5a89e8fb4c0d2856491825db9195221a2272e6e37ef3e1c29b","91422602c4065ad639ca8f951afb458195cca60226f1151eb3f0c592253cc90b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"55bf4dfc59b45564a1490ff7656007f1cedf5e047f547c59625f8b530298f4ef"}67{"parentUuid":"1063a400-d614-4f40-8523-4bf57df3431b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBku2HQeotQV5Jce7i","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":1772,"cache_read_input_tokens":36226,"output_tokens":951,"output_tokens_details":{"thinking_tokens":69},"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":1772},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":592,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsBkcQX58ZCjBWVznZw","requestRef":"820b6845-c468-4a91-abb2-1d40ff9c8806","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a8e654d7-3ed4-4af3-b4e1-61475be6e4a1","timestamp":"2026-10-30T05:25:51.604Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"a8e654d7-3ed4-4af3-b4e1-61475be6e4a1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsBku2HQeotQV5Jce7i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CgjnrtM8yhY5EWfnUGR7EV","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\nAll checks executed with ~/wp29/venv/bin/python on the regenerated out/results.json.\n| check | outcome |\n|---|---|\n| Clean re-run (results.json deleted, script re-run) byte-identical to previous output | PASS |\n| Derived 5 km CI [-0.843, -0.019] contains ATT -0.431 and is symmetric (±0.001 rounding) | PASS |\n| Wage CI [166, 2141] contains 1153; weeks CI [-2.24, -1.75] ordered and contains -1.99 | PASS |\n| All SEs (printed and derived) positive | PASS |\n| Derived wage SE 503.83 implies two-sided normal p = 0.022, equal to printed p=0.022 (supports z=1.96 convention) | PASS |\n| Star consistency with printed SEs (normal approx.): 5 km ATT ** (p≈0.040); HIV 5 km unstarred (p≈0.25); narcotics 3 km ***; fatal 1 km *** | PASS |\n| Derived 95% CI excludes 0, consistent with ** (p<0.05) | PASS |\n| Every non-boolean extracted/derived key has a locator | PASS |\n| n_input_rows equals wc -l (README 10, DCR 134, RESEA 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 [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: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0286/out/state/gate_receipts/20261030T052558.199398Z.json\n20261030T052558.199398Z.json","is_error":false}]},"uuid":"faed1e08-47c8-496f-a65a-40ece28426b8","timestamp":"2026-10-30T05:25:58.212Z","toolUseResult":{"stdout":"  [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: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0286/out/state/gate_receipts/20261030T052558.199398Z.json\n20261030T052558.199398Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0936aed5-059b-421f-9502-ed964a6abb44","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0286","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","hash":"60ca68f766ca21b7917b63a5e4257ebc93e84252f6cbb20255d4e8612ccf346c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01CgjnrtM8yhY5EWfnUGR7EV","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\nAll checks executed with ~/wp29/venv/bin/python on the regenerated out/results.json.\n| check | outcome |\n|---|---|\n| Clean re-run (results.json deleted, script re-run) byte-identical to previous output | PASS |\n| Derived 5 km CI [-0.843, -0.019] contains ATT -0.431 and is symmetric (±0.001 rounding) | PASS |\n| Wage CI [166, 2141] contains 1153; weeks CI [-2.24, -1.75] ordered and contains -1.99 | PASS |\n| All SEs (printed and derived) positive | PASS |\n| Derived wage SE 503.83 implies two-sided normal p = 0.022, equal to printed p=0.022 (supports z=1.96 convention) | PASS |\n| Star consistency with printed SEs (normal approx.): 5 km ATT ** (p≈0.040); HIV 5 km unstarred (p≈0.25); narcotics 3 km ***; fatal 1 km *** | PASS |\n| Derived 95% CI excludes 0, consistent with ** (p<0.05) | PASS |\n| Every non-boolean extracted/derived key has a locator | PASS |\n| n_input_rows equals wc -l (README 10, DCR 134, RESEA 58) | PASS |\n| Exclusion ledger: 22 − 0 = 22 = n_analysed | PASS |\n| Table 3 col 1 Wage = Table 2 ATE Wage (cross-check) | PASS |\n| Cross-table: Table 6 SE 0.210 vs Table A19 SE 0.218 for same estimate | SOURCE INCONSISTENCY — reported in data_issues, Table 6 used per key definition |\n| Cross-table: control regions 608 vs 640; Obs = (640+treated)×25 | SOURCE INCONSISTENCY — reported in data_issues |\nNote: JSON serialises 0.210 as 0.21 and -0.340 as -0.34 (trailing zeros cannot be represented in a JSON number); numeric values are identical to the printed ones.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30; ls out/state/gate_receipts","description":"Write checks file and run 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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, selected treatment effects, standard errors (SEs), confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1, a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (Tables 6 and 7), and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, a randomised evaluation of Rhode Island's RESEA programme (Table 2). Give each value a locator, derive one 95% CI from an SE and one SE from a 95% CI, and identify which requested quantity the source does not report. Nothing is re-estimated.\n\n## Data and exclusions\nThe inputs are the two plain-text extracts under `data/sources/` and `data/README.md`, which was read only for provenance. Line counts are `n_input_rows`: README 10, DCR extract 134, RESEA extract 58. All SHA-256 hashes match the README and the bundle (`out/state/data_audit.md`). No records were excluded. `out/exclusions.csv` holds a single row that removes nothing: 22 requested source items were assessed and 0 removed, so `n_analysed` = 22. Those 22 are 11 printed DCR values, the HIV p-value lookup, the HIV significance flag, 8 printed RESEA values and the RESEA SE-reported flag. The three derived values are computed from these items. The problems found in the sources are listed under `data_issues` and summarised under Limitations below.\n\n## Method\n`out/analysis.py` parses the table blocks from the text using the \" | \" cell separators. In the DCR tables, the line after each coefficient row holds the SEs in parentheses. Each value is taken from the table the key names, and the script prints every source line it matched. Significance stars are counted to set `dcr_hiv_att_5km_significant_10pct`. The script searches the whole DCR extract for an exact printed p-value (\"p = 0.x\") and finds none. Derived quantities (conventions in `derivation_notes`):\n- 95% CI = ATT ± 1.96 × SE, using the Table 6 printed ATT and SE, rounded to 3 decimals.\n- SE = (upper − lower) / (2 × 1.96), using the Table 2 printed wage-ATE CI, rounded to 2 decimals.\n\nThe code uses no randomness (`random_seed` = null).\n\n## Results\nDCR study (arXiv:2609.27732v1), log points:\n- Table 6, 5 km: ATT `dcr_att_5km` = -0.431 (\\*\\*), SE `dcr_att_5km_se` = 0.210. Observations `dcr_obs_5km` = 17250.\n- Table 6, 3 km: ATT `dcr_att_3km` = -0.283, SE `dcr_att_3km_se` = 0.279.\n- Table 7, Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549 (\\*\\*\\*), SE `dcr_narcotics_att_3km_se` = 0.187.\n- Table 7, Drug-related (fatal), 1 km: `dcr_fatal_att_1km` = -1.207 (\\*\\*\\*), SE `dcr_fatal_att_1km_se` = 0.137.\n- Table 7, HIV, 5 km: `dcr_hiv_att_5km` = -0.340, SE `dcr_hiv_att_5km_se` = 0.294.\n  - The source does not print an exact p-value, so `dcr_hiv_att_5km_p_value` = null. The paper reports significance only through stars.\n  - The estimate has no star, so `dcr_hiv_att_5km_significant_10pct` = false.\n- Derived 95% CI for the 5 km ATT: [`dcr_att_5km_ci_low` = -0.843, `dcr_att_5km_ci_high` = -0.019].\n\nRESEA study (arXiv:2606.14621v1), Table 2:\n- Wage ATE `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n  - The table prints no SE (`ri_wage_ate_se_reported` = false).\n  - Derived SE `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage `ri_control_mean_wage` = 33043 USD/yr.\n- Reemployment ATE `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE `ri_weeks_ui_ate` = -1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = -2.24, `ri_weeks_ui_ate_ci_high` = -1.75].\n\n`feasibility` = \"complete\". Every requested quantity was either extracted, derived, or correctly identified as not reported (the HIV p-value).\n\nNote on format: a JSON number cannot keep trailing zeros, so `results.json` stores 0.210 as 0.21 and -0.340 as -0.34. The numeric values are identical, and the printed forms (0.210, -0.340) are shown above.\n\n## Uncertainty\n- **Standard errors.** In the DCR paper (Tables 6 and 7) the uncertainty is given as SEs clustered at the MedStat region level, printed in parentheses, plus significance stars. It prints no CIs or exact p-values for these cells.\n- **Confidence intervals.** In the RESEA paper (Table 2) the uncertainty for each ATE is a two-sided p-value and a 95% CI \"based on the methodology described in the main text\". That methodology is not in the extract, and no SE is printed.\n- **What the derived CI assumes.** The 5 km CI assumes the CS-DR estimator is approximately normal and that the clustered SE is valid, using a normal critical value of 1.96. It is not a source value. Using the Table A19 SE (0.218) for the same estimate would widen it. A t critical value with few clusters would also widen it, but the number of clusters behind the SE is not stated in the extract.\n- **What the derived SE assumes.** The wage SE assumes the printed CI is a symmetric Wald interval with z = 1.96. Two things support this: the interval is symmetric (1153 − 166 = 987 and 2141 − 1153 = 988), and the SE reproduces the printed p = 0.022 (checked in `out/state/checks.md`). With N = 23,549, a t critical value would make no material difference. The CI bounds are rounded to whole dollars, which makes the derived SE uncertain by about ±0.3 USD.\n\n## Limitations and open issues\n- **Inconsistencies in the sources.** These are recorded in `data_issues` and none of them changed the extraction rule.\n  1. Table 6 prints an SE of 0.210 for the 5 km ATT, while Table A19 prints 0.218 for the same CS-DR estimate. I used Table 6, as the key requires.\n  2. Tables 6 and 7 say there are 608 control regions, while the Table A19 note says 640. The Table 6 Observations equal (640 + treated regions) × 25 in every column. The 25-period panel length is my inference; the extract does not state it.\n  3. The prose sometimes differs from the tables: the abstract's 35% fall in fatal outcomes against about 30% implied by -0.354, and HIV described as \"close to zero\" against -0.340. In the RESEA text, the weeks-on-UI CI is given as positive magnitudes.\n  4. There is a rendering defect (\"p ¡ 0.001\"), and RESEA Tables 2–3 were re-set by the extract editor.\n- **Limits on verification.** \"As printed\" can only be checked against the extracts, which may contain transcription errors relative to the original papers. I did not consult the originals, as the task rules require.\n- **Derived values.** These are conventions, not values from the sources (see Uncertainty).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints a 5 km ATT of -0.431** (SE 0.210) on 17,250 observations and a 3 km ATT of -0.283 (SE 0.279).\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_obs_5km\",\"dcr_att_3km\",\"dcr_att_3km_se\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same 5 km estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km 0.549*** (0.187), Drug-related (fatal) 1 km -1.207*** (0.137), and HIV 5 km -0.340 (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 drops trailing zeros (-0.34); numeric value identical.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the DCR extract (null), and the estimate is not marked significant at the 10% level.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"Only significance stars are printed.\"},\n {\"id\":\"C4\",\"text\":\"A normal-approximation 95% CI for the Table 6 5 km ATT, derived as ATT ± 1.96×SE, is [-0.843, -0.019].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; assumes normality and uses Table 6 SE 0.210.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arXiv:2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], a control mean wage of $33,043, 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_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Table re-set by extract editor; text gives the weeks CI as positive magnitudes.\"},\n {\"id\":\"C6\",\"text\":\"No SE is printed for the wage ATE; the SE implied by the printed 95% CI with z=1.96 is 503.83 USD, which reproduces the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric Wald CI; CI bounds rounded to whole dollars (±~0.3 USD).\"},\n {\"id\":\"C7\",\"text\":\"The DCR source is internally inconsistent on the 5 km SE (0.210 vs 0.218) and the number of control regions (608 vs 640, with observation counts matching 640).\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"The 25-period panel length used to reconcile observations is an inference.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && echo ok","description":"Write report and claims 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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, selected treatment effects, standard errors (SEs), confidence-interval (CI) bounds and counts from (i) Armendariz (2026), arXiv:2609.27732v1, a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (Tables 6 and 7), and (ii) Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, a randomised evaluation of Rhode Island's RESEA programme (Table 2). Give each value a locator, derive one 95% CI from an SE and one SE from a 95% CI, and identify which requested quantity the source does not report. Nothing is re-estimated.\n\n## Data and exclusions\nThe inputs are the two plain-text extracts under `data/sources/` and `data/README.md`, which was read only for provenance. Line counts are `n_input_rows`: README 10, DCR extract 134, RESEA extract 58. All SHA-256 hashes match the README and the bundle (`out/state/data_audit.md`). No records were excluded. `out/exclusions.csv` holds a single row that removes nothing: 22 requested source items were assessed and 0 removed, so `n_analysed` = 22. Those 22 are 11 printed DCR values, the HIV p-value lookup, the HIV significance flag, 8 printed RESEA values and the RESEA SE-reported flag. The three derived values are computed from these items. The problems found in the sources are listed under `data_issues` and summarised under Limitations below.\n\n## Method\n`out/analysis.py` parses the table blocks from the text using the \" | \" cell separators. In the DCR tables, the line after each coefficient row holds the SEs in parentheses. Each value is taken from the table the key names, and the script prints every source line it matched. Significance stars are counted to set `dcr_hiv_att_5km_significant_10pct`. The script searches the whole DCR extract for an exact printed p-value (\"p = 0.x\") and finds none. Derived quantities (conventions in `derivation_notes`):\n- 95% CI = ATT ± 1.96 × SE, using the Table 6 printed ATT and SE, rounded to 3 decimals.\n- SE = (upper − lower) / (2 × 1.96), using the Table 2 printed wage-ATE CI, rounded to 2 decimals.\n\nThe code uses no randomness (`random_seed` = null).\n\n## Results\nDCR study (arXiv:2609.27732v1), log points:\n- Table 6, 5 km: ATT `dcr_att_5km` = -0.431 (\\*\\*), SE `dcr_att_5km_se` = 0.210. Observations `dcr_obs_5km` = 17250.\n- Table 6, 3 km: ATT `dcr_att_3km` = -0.283, SE `dcr_att_3km_se` = 0.279.\n- Table 7, Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549 (\\*\\*\\*), SE `dcr_narcotics_att_3km_se` = 0.187.\n- Table 7, Drug-related (fatal), 1 km: `dcr_fatal_att_1km` = -1.207 (\\*\\*\\*), SE `dcr_fatal_att_1km_se` = 0.137.\n- Table 7, HIV, 5 km: `dcr_hiv_att_5km` = -0.340, SE `dcr_hiv_att_5km_se` = 0.294.\n  - The source does not print an exact p-value, so `dcr_hiv_att_5km_p_value` = null. The paper reports significance only through stars.\n  - The estimate has no star, so `dcr_hiv_att_5km_significant_10pct` = false.\n- Derived 95% CI for the 5 km ATT: [`dcr_att_5km_ci_low` = -0.843, `dcr_att_5km_ci_high` = -0.019].\n\nRESEA study (arXiv:2606.14621v1), Table 2:\n- Wage ATE `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n  - The table prints no SE (`ri_wage_ate_se_reported` = false).\n  - Derived SE `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage `ri_control_mean_wage` = 33043 USD/yr.\n- Reemployment ATE `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE `ri_weeks_ui_ate` = -1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = -2.24, `ri_weeks_ui_ate_ci_high` = -1.75].\n\n`feasibility` = \"complete\". Every requested quantity was either extracted, derived, or correctly identified as not reported (the HIV p-value).\n\nNote on format: a JSON number cannot keep trailing zeros, so `results.json` stores 0.210 as 0.21 and -0.340 as -0.34. The numeric values are identical, and the printed forms (0.210, -0.340) are shown above.\n\n## Uncertainty\n- **Standard errors.** In the DCR paper (Tables 6 and 7) the uncertainty is given as SEs clustered at the MedStat region level, printed in parentheses, plus significance stars. It prints no CIs or exact p-values for these cells.\n- **Confidence intervals.** In the RESEA paper (Table 2) the uncertainty for each ATE is a two-sided p-value and a 95% CI \"based on the methodology described in the main text\". That methodology is not in the extract, and no SE is printed.\n- **What the derived CI assumes.** The 5 km CI assumes the CS-DR estimator is approximately normal and that the clustered SE is valid, using a normal critical value of 1.96. It is not a source value. Using the Table A19 SE (0.218) for the same estimate would widen it. A t critical value with few clusters would also widen it, but the number of clusters behind the SE is not stated in the extract.\n- **What the derived SE assumes.** The wage SE assumes the printed CI is a symmetric Wald interval with z = 1.96. Two things support this: the interval is symmetric (1153 − 166 = 987 and 2141 − 1153 = 988), and the SE reproduces the printed p = 0.022 (checked in `out/state/checks.md`). With N = 23,549, a t critical value would make no material difference. The CI bounds are rounded to whole dollars, which makes the derived SE uncertain by about ±0.3 USD.\n\n## Limitations and open issues\n- **Inconsistencies in the sources.** These are recorded in `data_issues` and none of them changed the extraction rule.\n  1. Table 6 prints an SE of 0.210 for the 5 km ATT, while Table A19 prints 0.218 for the same CS-DR estimate. I used Table 6, as the key requires.\n  2. Tables 6 and 7 say there are 608 control regions, while the Table A19 note says 640. The Table 6 Observations equal (640 + treated regions) × 25 in every column. The 25-period panel length is my inference; the extract does not state it.\n  3. The prose sometimes differs from the tables: the abstract's 35% fall in fatal outcomes against about 30% implied by -0.354, and HIV described as \"close to zero\" against -0.340. In the RESEA text, the weeks-on-UI CI is given as positive magnitudes.\n  4. There is a rendering defect (\"p ¡ 0.001\"), and RESEA Tables 2–3 were re-set by the extract editor.\n- **Limits on verification.** \"As printed\" can only be checked against the extracts, which may contain transcription errors relative to the original papers. I did not consult the originals, as the task rules require.\n- **Derived values.** These are conventions, not values from the sources (see Uncertainty).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arXiv:2609.27732v1 prints a 5 km ATT of -0.431** (SE 0.210) on 17,250 observations and a 3 km ATT of -0.283 (SE 0.279).\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_obs_5km\",\"dcr_att_3km\",\"dcr_att_3km_se\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same 5 km estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km 0.549*** (0.187), Drug-related (fatal) 1 km -1.207*** (0.137), and HIV 5 km -0.340 (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 drops trailing zeros (-0.34); numeric value identical.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the DCR extract (null), and the estimate is not marked significant at the 10% level.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"Only significance stars are printed.\"},\n {\"id\":\"C4\",\"text\":\"A normal-approximation 95% CI for the Table 6 5 km ATT, derived as ATT ± 1.96×SE, is [-0.843, -0.019].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Derived, not printed; assumes normality and uses Table 6 SE 0.210.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arXiv:2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], a control mean wage of $33,043, 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_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Table re-set by extract editor; text gives the weeks CI as positive magnitudes.\"},\n {\"id\":\"C6\",\"text\":\"No SE is printed for the wage ATE; the SE implied by the printed 95% CI with z=1.96 is 503.83 USD, which reproduces the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"derivation_notes\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric Wald CI; CI bounds rounded to whole dollars (±~0.3 USD).\"},\n {\"id\":\"C7\",\"text\":\"The DCR source is internally inconsistent on the 5 km SE (0.210 vs 0.218) and the number of control regions (608 vs 640, with observation counts matching 640).\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"The 25-period panel length used to reconcile observations is an inference.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\" && echo ok","description":"Write report and claims ledger"}}]}}76{"type":"api-request","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194","id":"83f7aaec-f526-425b-94bd-338a2b2bc640","timestamp":"2026-10-30T05:26:32.344Z","version":"2.1.314","querySource":"sdk","shapeHash":"6cadd754b990b914a32307d3fb20fcdbaf3f1a460ab21338160883bccbb38440","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":"47969229-e81e-474c-8218-33b34cbb1129","keep":22,"tail":["e14d62eba3feda861935e0a919c15a2a05a9665a117a90499317e0da61aa5cb3","eeaba7f89827589e37331906c008ff734c0c3d646be7865fb8c2dffb003c0087"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"940ef0c4429a6120a3d65375c706f48a2dcdc0fa2dca5f31589aca7cb6499989"}77{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4457f2d1-e4ab-4886-a7fc-9f4c07bd7dae","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194"}78{"type":"atis-latch","atis":"","sessionId":"2d8d7da4-1d2f-4d1a-974c-a8592c7c9194"}79{"parentUuid":"4457f2d1-e4ab-4886-a7fc-9f4c07bd7dae","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsEvczxw4uxYZytPHCp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ABEU97JcJ7FTw1wnuuZWLH","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; 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the final gate run passed (0 of 48 checks failed). A clean re-run of `out/analysis.py` gives a byte-identical `out/results.json`.\n\n**DCR study (Switzerland), log points**\n\n| Table | Cell | Estimate | Standard error |\n|---|---|---|---|\n| 6 | 5 km | -0.431** | 0.210 |\n| 6 | 3 km | -0.283 | 0.279 |\n| 7 | Narcotics, 3 km | 0.549*** | 0.187 |\n| 7 | Drug-related (fatal), 1 km | -1.207*** | 0.137 |\n| 7 | HIV, 5 km | -0.340 | 0.294 |\n\n- **5 km observations:** 17,250.\n- **HIV 5 km:** the paper prints no exact p-value, so that key is `null`. It has no significance star, so it is not marked significant at 10%.\n- **Derived 95% interval for the 5 km effect:** [-0.843, -0.019], computed as estimate ± 1.96 × standard error.\n\n**RESEA study (Rhode Island), Table 2**\n- **Wage effect:** $1,153 a year, 95% interval [166, 2,141]. No standard error is printed. The one implied by the interval, at 1.96, is 503.83; it reproduces the printed p = 0.022.\n- **Control-group mean wage:** $33,043.\n- **Reemployment:** +1.47 percentage points.\n- **Weeks on unemployment insurance:** -1.99 weeks, interval [-2.24, -1.75].\n\n**Inconsistencies in the sources** (logged in `data_issues`; none changed what was extracted):\n- **5 km standard error:** Table 6 prints 0.210, while Table A19 prints 0.218 for the same estimate. I used Table 6, as the task requires.\n- **Number of comparison regions:** the main tables say 608, but the Table A19 note says 640. The observation counts match 640 + treated regions × 25 years. The 25-year panel length is my inference; the extract doesn't state it.\n- **Text versus tables:** the DCR abstract's \"35%\" fall in fatal outcomes doesn't match the table, which implies about 30%. The RESEA text gives the weeks-on-UI interval as positive numbers, while Table 2 prints negative bounds.\n- **Formatting:** the RESEA text has one rendering defect (\"p ¡ 0.001\").\n\n**Other limitations**\n- **Trailing zeros:** JSON numbers can't keep them, so `results.json` stores 0.210 as 0.21 and -0.340 as -0.34. The values are the same, and the report shows the printed forms.\n- **Assumptions behind the derived values:** both assume an approximately normal estimate with a symmetric interval at 1.96. The paper doesn't print either value.\n- **Checked against the extracts only:** \"as printed\" means as printed in the extracts. They could differ from the original papers, which the task rules forbid consulting.\n\nEverything is in `out/`; open `report.md` first. 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