spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:25:31.435Z","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:25:31.441Z","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}4{"parentUuid":null,"isSidechain":false,"promptId":"82d16e65-fda4-438c-8139-8fd74dfb1c5b","type":"user","message":{"role":"user","content":"Begin"},"uuid":"beabf13b-4b61-4285-af67-f3a508163956","timestamp":"2026-10-30T05:25:31.446Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","shapeHash":"ed2217a4794bcef594a0d666c1d3abc7d8f1f74c3de730245e10e9ba0c4acaf0","timestamp":"2026-10-30T05:25:31.459Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0287\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"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/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}12{"parentUuid":"cc281216-df35-4381-bb8c-f67c4e5871a9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAV7iLyWRupVdqGSL9","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":2209,"cache_read_input_tokens":1504,"output_tokens":125,"output_tokens_details":{"thinking_tokens":19},"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":499,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsAUqM7pLc2YsARUEnr","requestRef":"cdfc168a-97fd-4522-89a4-45152a8c2dc6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"11e839d5-fb7a-43e3-9bcb-e3be85be74a8","timestamp":"2026-10-30T05:25:33.075Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"11e839d5-fb7a-43e3-9bcb-e3be85be74a8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsAV7iLyWRupVdqGSL9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01N5xmu9NrartE5TNAJZVGom","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  160 simon-pierreboucher  staff  5120 oct.  30 01:25 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\ndata:\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":false}]},"uuid":"df5e9c88-c705-4d7e-b67d-4c5b3af6b4bc","timestamp":"2026-10-30T05:25:33.644Z","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  160 simon-pierreboucher  staff  5120 oct.  30 01:25 ..\n-rw-r--r--    1 simon-pierreboucher  staff   641 oct.  29 23:45 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:45 data\n-rw-r--r--    1 simon-pierreboucher  staff  3334 oct.  29 23:45 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  8459 oct.  29 23:45 TASK.md\ndata:\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"39eca7fe-cb09-4e6d-99ce-45311df4947e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","hash":"9099adc4468300fb1c85930373b2d2ba6c3cac3e3897465ebd59773760503b49","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01N5xmu9NrartE5TNAJZVGom","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      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\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","is_error":false}]},"uuid":"44b35732-0135-44f6-b0ee-401aa81b2783","timestamp":"2026-10-30T05:25:35.573Z","toolUseResult":{"stdout":"# 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      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\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0af39267-4d17-4b0f-b409-0ed3cef55ca8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","hash":"062a335bd56fe774c0cf14ac8c133ed71b43154a636a05b53d9cc9d2bc3838dc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LfQjcHUqcg7edKBnckXfvF","name":"Bash","input":{"command":"cat data/README.md; 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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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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; cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed treatment effects/uncertainty from two source extracts.\nReads only data/; writes out/results.json deterministically. No randomness (random_seed = null).\nEvery extracted value is parsed from the text and the matched source line is printed.\"\"\"\nimport json, re, os, hashlib\nfrom pathlib import Path\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\"\nDCR = DATA / \"sources\" / \"arxiv-2609.27732v1.txt\"\nRI = DATA / \"sources\" / \"arxiv-2606.14621v1.txt\"\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95%\n\ndef lines(p):\n    return p.read_text(encoding=\"utf-8\").split(\"\\n\")\n\ndef num(s):\n    \"\"\"Parse a printed number keeping its printed decimals (e.g. '-0.431**' -> -0.431, '$1,153' -> 1153).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"%\", \"\")\n    s = re.sub(r\"\\*+$\", \"\", s).strip()\n    s = s.strip(\"()\")\n    return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef stars(s):\n    m = re.search(r\"(\\*+)\\s*$\", s.strip()); return len(m.group(1)) if m else 0\n\ndef block(ls, start_pat, end_pat):\n    i0 = next(i for i, l in enumerate(ls) if re.match(start_pat, l))\n    i1 = next(i for i in range(i0 + 1, len(ls)) if re.match(end_pat, ls[i]))\n    return i0, ls[i0:i1]\n\ndef parse_reg_table(ls, title_pat, end_pat):\n    \"\"\"Return header columns, {row: {col: (raw_coef, raw_se, lineno)}}, {row: {col: raw}} for non-coef rows.\"\"\"\n    i0, blk = block(ls, title_pat, end_pat)\n    rows = [(i0 + k + 1, l) for k, l in enumerate(blk) if l.strip() and \" | \" in l or l.startswith(\"|\")]\n    rows = [(n, l) for n, l in rows if l.strip()]\n    header = [c.strip() for c in rows[0][1].split(\"|\")][1:]\n    coef, other = {}, {}\n    k = 1\n    while k < len(rows):\n        n, l = rows[k]\n        cells = [c.strip() for c in l.split(\"|\")]\n        label, vals = cells[0], cells[1:]\n        nxt = rows[k + 1][1] if k + 1 < len(rows) else \"\"\n        if label and nxt.strip().startswith(\"|\") and \"(\" in nxt:\n            ses = [c.strip() for c in nxt.split(\"|\")][1:]\n            coef[label] = {h: (v, s, n, rows[k + 1][0]) for h, v, s in zip(header, vals, ses)}\n            k += 2\n        else:\n            other[label] = {h: (v, n) for h, v in zip(header, vals)}\n            k += 1\n    return header, coef, other\n\ndl = lines(DCR); rl = lines(RI)\nh6, t6, o6 = parse_reg_table(dl, r\"^Table 6:\", r\"^Note:\")\nh7, t7, o7 = parse_reg_table(dl, r\"^Table 7:\", r\"^Note:\")\nhA, tA, oA = parse_reg_table(dl, r\"^Table A19:\", r\"^Note:\")\nif h7 and h7[0] == \"Outcome\":  # Table 7 header line starts with 'Outcome |'\n    pass\n# Table 7 header has 'Outcome' as first cell rather than empty: re-parse header\n_, blk7 = block(dl, r\"^Table 7:\", r\"^Note:\")\nhdr7 = next(l for l in blk7 if l.startswith(\"Outcome |\"))\nh7 = [c.strip() for c in hdr7.split(\"|\")][1:]\n_, t7, o7 = (None, *parse_reg_table(dl, r\"^Table 7:\", r\"^Note:\")[1:])\n# re-key Table 7 entries with the correct header\ndef rekey(tab, hdr):\n    out = {}\n    for r, d in tab.items():\n        vals = list(d.values())\n        out[r] = dict(zip(hdr, vals))\n    return out\nt7 = rekey(t7, h7); o7 = rekey(o7, h7)\n\ndef show(src, n):\n    print(f\"  [{src} line {n}] {(dl if src == DCR_ID else rl)[n - 1]}\")\n\nres, loc = {}, {}\ndef put_coef(key, tab, tabname, row, col):\n    v, s, n1, n2 = tab[row][col]\n    res[key] = num(v); res[key + \"_se\"] = num(s)\n    loc[key] = f'{DCR_ID} | Table {tabname} | row \"{row}\" | column \"{col}\"'\n    loc[key + \"_se\"] = f'{DCR_ID} | Table {tabname} | row \"{row}\" (standard-error line below, in parentheses) | column \"{col}\"'\n    print(f\"{key} = {res[key]} (stars={stars(v)}), {key}_se = {res[key + '_se']}\"); show(DCR_ID, n1); show(DCR_ID, n2)\n    return stars(v)\n\nput_coef(\"dcr_att_5km\", t6, \"6\", \"ATT\", \"5 km\")\nput_coef(\"dcr_att_3km\", t6, \"6\", \"ATT\", \"3 km\")\nv, n = o6[\"Observations\"][\"5 km\"]\nres[\"dcr_obs_5km\"] = num(v)\nloc[\"dcr_obs_5km\"] = f'{DCR_ID} | Table 6 | row \"Observations\" | column \"5 km\"'\nprint(f\"dcr_obs_5km = {res['dcr_obs_5km']}\"); show(DCR_ID, n)\nput_coef(\"dcr_narcotics_att_3km\", t7, \"7\", \"Narcotics\", \"3 km\")\nput_coef(\"dcr_fatal_att_1km\", t7, \"7\", \"Drug-related (fatal)\", \"1 km\")\nhiv_stars = put_coef(\"dcr_hiv_att_5km\", t7, \"7\", \"HIV\", \"5 km\")\n\n# exact p-value: search the whole DCR extract for any printed p-value\npv_lines = [i + 1 for i, l in enumerate(dl) if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\nprint(f\"lines in {DCR_ID} printing an exact p-value (p = ...): {pv_lines}\")\nres[\"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 clustered SE \"\n                                  f\"and significance stars; no exact p-value is printed anywhere in the extract)\")\nres[\"dcr_hiv_att_5km_significant_10pct\"] = hiv_stars >= 1\nprint(f\"dcr_hiv_att_5km_significant_10pct = {res['dcr_hiv_att_5km_significant_10pct']} (stars on Table 7 HIV 5 km cell: {hiv_stars})\")\n\n# derived 95% CI from Table 6 printed SE, normal approximation\nb, se = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\nres[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 4)\nres[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 4)\nloc[\"dcr_att_5km_ci_low\"] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived (estimate - 1.96 x printed SE)'\nloc[\"dcr_att_5km_ci_high\"] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived (estimate + 1.96 x printed SE)'\n\n# ---- RESEA Table 2 ----\n_, blk2 = block(rl, r\"^\\[Table 2\", r\"^Table 2:\")\ni2 = next(i for i, l in enumerate(rl) if l.startswith(\"[Table 2\"))\nhdr = next(l for l in blk2 if l.startswith(\"Outcome |\"))\nH2 = [c.strip() for c in hdr.split(\"|\")]\nT2 = {}\nfor k, l in enumerate(blk2):\n    if \" | \" in l and not l.startswith(\"Outcome |\") and not l.startswith(\"[\"):\n        cells = [c.strip() for c in l.split(\"|\")]\n        T2[cells[0]] = (dict(zip(H2, cells)), i2 + k + 1)\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*([^\\]]+)\\]$\",\n                 cell.replace(\"-$\", \"-\"))\n    if not m:\n        raise ValueError(cell)\n    est = num(m.group(1) + m.group(2))\n    # split CI bounds robustly (thousands separators: \"166, 2,141\")\n    ci = cell.split(\"95% CI:\")[1].strip().strip(\"[]\")\n    parts = re.findall(r\"-?\\d{1,3}(?:,\\d{3})+|-?\\d+(?:\\.\\d+)?\", ci)\n    return est, num(parts[0]), num(parts[1]), m.group(4) + m.group(5)\n\nATE = \"Average treatment effect estimate\"\nfor key, row in [(\"ri_wage_ate\", \"Wage\"), (\"ri_reemployment_ate_pp\", \"Reemployment\"), (\"ri_weeks_ui_ate\", \"Weeks on UI\")]:\n    d, n = T2[row]\n    est, lo, hi, p = parse_effect(d[ATE])\n    res[key] = est\n    loc[key] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (point estimate)'\n    print(f\"{key} = {est}; printed 95% CI [{lo}, {hi}]; p{p}\"); print(f\"  [{RI_ID} line {n}] {rl[n - 1]}\")\n    if key in (\"ri_wage_ate\", \"ri_weeks_ui_ate\"):\n        res[key + \"_ci_low\"], res[key + \"_ci_high\"] = lo, hi\n        loc[key + \"_ci_low\"] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (95% CI lower bound)'\n        loc[key + \"_ci_high\"] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (95% CI upper bound)'\nd, n = T2[\"Wage\"]\ncm = re.match(r\"^\\$([\\d,]+)\", d[\"Control\"]).group(1)\nres[\"ri_control_mean_wage\"] = num(cm)\nloc[\"ri_control_mean_wage\"] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"Control\" (mean)'\nprint(f\"ri_control_mean_wage = {res['ri_control_mean_wage']}\"); print(f\"  [{RI_ID} line {n}] {rl[n - 1]}\")\n\nse_in_t2 = bool(re.search(r\"\\bSE\\b|standard error\", \" \".join(blk2), re.I)) and False  # see below\nwage_cell = T2[\"Wage\"][0][ATE]\nres[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\(\\s*[\\d,\\.]+\\s*\\)|\\bSE\\b|s\\.e\\.\", wage_cell, re.I))\nprint(f\"ri_wage_ate_se_reported = {res['ri_wage_ate_se_reported']} (wage ATE cell: '{wage_cell}')\")\nlo, hi = res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"]\nres[\"ri_wage_ate_se_derived\"] = round((hi - lo) / (2 * Z), 2)\nloc[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived '\n                                 f'((CI upper - CI lower) / (2 x 1.96))')\n\n# ---- consistency checks that feed data_issues ----\nissues = []\nseA = num(tA[\"ATT\"][\"CS-DR\"][1])\nif seA != res[\"dcr_att_5km_se\"]:\n    issues.append({\"issue\": (f\"{DCR_ID}: the same 5 km CS-DR overall ATT ({res['dcr_att_5km']}) is printed with SE \"\n                             f\"{res['dcr_att_5km_se']} in Table 6 but SE {seA} in Appendix Table A19 (column 'CS-DR').\"),\n                   \"handling\": \"Used Table 6 as the key definitions require (dcr_att_5km_se, derived CI); A19 value not used.\"})\nctrl6 = num(o6[\"Control regions\"][\"5 km\"][0])\nmA = re.search(r\"never-treated comparison regions \\((\\d+) regions\\)\", \"\\n\".join(dl))\nif mA and int(mA.group(1)) != ctrl6:\n    issues.append({\"issue\": (f\"{DCR_ID}: Tables 6/7 and A19 rows report {ctrl6} never-treated control regions, but the \"\n                             f\"Table A19 note says {mA.group(1)} never-treated comparison regions.\"),\n                   \"handling\": \"Recorded only; no requested key depends on the control-region count.\"})\nfatal5 = num(t7[\"Drug-related (fatal)\"][\"5 km\"][0])\nissues.append({\"issue\": (f\"{DCR_ID}: the abstract says fatal hospitalisations 'fall by 35%', whereas Table 7 gives a 5 km \"\n                         f\"fatal ATT of {fatal5} log points (exp({fatal5})-1 = {100 * (2.718281828459045 ** fatal5 - 1):.1f}%), \"\n                         f\"and Section 6.1 says 'approximately 30% at 5 km'; the abstract figure appears to equate log points with percent.\"),\n               \"handling\": \"Recorded only; values were taken from the tables named in the key definitions.\"})\nhiv5 = res[\"dcr_hiv_att_5km\"]\nissues.append({\"issue\": (f\"{DCR_ID}: the text describes HIV estimates as 'insignificant and close to zero', but the printed \"\n                         f\"5 km HIV ATT is {hiv5} log points (SE {res['dcr_hiv_att_5km_se']}); it is insignificant (no star) but not numerically close to zero.\"),\n               \"handling\": \"Recorded only; significance flag set from the absence of stars in Table 7.\"})\nissues.append({\"issue\": (f\"{RI_ID}: the Results text states the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' for a \"\n                         f\"'1.99 week reduction', while Table 2 prints the ATE as -1.99 with CI [-2.24, -1.75].\"),\n               \"handling\": \"Used the signed Table 2 values as required (lower bound first).\"})\nif any(\"¡\" in l for l in rl):\n    issues.append({\"issue\": f\"{RI_ID}: rendering defect in the Section 3 text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Not used for any key (Table 2 cell prints 'p<0.001').\"})\nissues.append({\"issue\": (f\"{RI_ID}: Tables 2 and 3 were re-set by the extract editor into a one-row-per-outcome layout \"\n                         f\"(HTML rendering split rows and detached p-values).\"),\n               \"handling\": \"Relied on the editor's statement that values are verbatim; Table 3 'Difference in means' column matches Table 2 ATE column exactly (checked).\"})\n# check Table 3 duplicates Table 2 ATEs\n_, blk3 = block(rl, r\"^\\[Table 3\", r\"^Table 3:\")\nt3w = next(l for l in blk3 if l.startswith(\"Wage |\")).split(\"|\")[1].strip()\nassert parse_effect(t3w)[:3] == (res[\"ri_wage_ate\"], lo, hi), \"Table 3 vs Table 2 wage mismatch\"\n\ndef wc_l(p):\n    return p.read_bytes().count(b\"\\n\")\n\nout = {\n    \"task_id\": \"F4-conf-01\",\n    **{k: res[k] for k in [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n                           \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n                           \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\",\n                           \"dcr_hiv_att_5km_significant_10pct\", \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\",\n                           \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\", \"ri_wage_ate_se_reported\",\n                           \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n                           \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]},\n    \"locators\": loc,\n    \"derivation_notes\": {\n        \"dcr_att_5km_ci\": (\"95% CI = ATT +/- 1.96 x SE using the Table 6 printed ATT (-0.431) and clustered SE (0.210); \"\n                           \"normal (large-sample) critical value 1.96; rounded to 4 decimals. Assumes asymptotic normality of the \"\n                           \"CS-DR estimator and that the printed SE (itself rounded to 3 decimals) is the relevant SE.\"),\n        \"ri_wage_ate_se\": (\"SE = (upper - lower) / (2 x 1.96) using the Table 2 printed 95% CI [166, 2,141]; normal critical value \"\n                           \"1.96 (with N = 23,549 a t critical value is indistinguishable); rounded to 2 decimals. Assumes the \"\n                           \"printed CI is a symmetric Wald interval (midpoint 1153.5 vs estimate 1,153, consistent up to rounding).\"),\n    },\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"README.md\": wc_l(DATA / \"README.md\"), DCR.name: wc_l(DCR), RI.name: wc_l(RI)},\n    \"n_analysed\": 25,\n    \"random_seed\": None,\n}\nos.makedirs(ROOT / \"out\", exist_ok=True)\n(ROOT / \"out\" / \"results.json\").write_text(json.dumps(out, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\nprint(\"wrote out/results.json\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run the extraction script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":7332,"cache_read_input_tokens":13653,"output_tokens":7959,"output_tokens_details":{"thinking_tokens":1186},"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":7332},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsB2ZZqsKNdsu1ftFNb","requestRef":"9ca45730-bafb-43e0-966e-a42e97f4e49a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5859de5a-4040-4ec8-a4be-09b9a2ebd6bb","timestamp":"2026-10-30T05:26:44.275Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"5859de5a-4040-4ec8-a4be-09b9a2ebd6bb","isSidechain":false,"promptId":"82d16e65-fda4-438c-8139-8fd74dfb1c5b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01E9ajCTbxDJxxoAURqL524V","type":"tool_result","content":"dcr_att_5km = -0.431 (stars=2), dcr_att_5km_se = 0.21\n  [arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n  [arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\ndcr_att_3km = -0.283 (stars=0), dcr_att_3km_se = 0.279\n  [arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n  [arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\ndcr_obs_5km = 17250\n  [arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\ndcr_narcotics_att_3km = 0.549 (stars=3), dcr_narcotics_att_3km_se = 0.187\n  [arxiv-2609.27732v1 line 82] Narcotics | 1.186*** | 0.549*** | 0.390**\n  [arxiv-2609.27732v1 line 84] | (0.288) | (0.187) | (0.165)\ndcr_fatal_att_1km = -1.207 (stars=3), dcr_fatal_att_1km_se = 0.137\n  [arxiv-2609.27732v1 line 86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n  [arxiv-2609.27732v1 line 88] | (0.137) | (0.215) | (0.178)\ndcr_hiv_att_5km = -0.34 (stars=0), dcr_hiv_att_5km_se = 0.294\n  [arxiv-2609.27732v1 line 78] HIV | 0.063 | -0.239 | -0.340\n  [arxiv-2609.27732v1 line 80] | (0.801) | (0.337) | (0.294)\nlines in arxiv-2609.27732v1 printing an exact p-value (p = ...): []\ndcr_hiv_att_5km_significant_10pct = False (stars on Table 7 HIV 5 km cell: 0)\nri_wage_ate = 1153; printed 95% CI [166, 2141]; p=0.022\n  [arxiv-2606.14621v1 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]\nri_reemployment_ate_pp = 1.47; printed 95% CI [0.32, 2.62]; p=0.013\n  [arxiv-2606.14621v1 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]\nri_weeks_ui_ate = -1.99; printed 95% CI [-2.24, -1.75]; p<0.001\n  [arxiv-2606.14621v1 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]\nri_control_mean_wage = 33043\n  [arxiv-2606.14621v1 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]\nri_wage_ate_se_reported = False (wage ATE cell: '$1,153; p=0.022; 95% CI: [166, 2,141]')\nwrote out/results.json","is_error":false}]},"uuid":"b87d671e-5c42-40e5-92bf-723ba094895d","timestamp":"2026-10-30T05:26:44.318Z","toolUseResult":{"stdout":"dcr_att_5km = -0.431 (stars=2), dcr_att_5km_se = 0.21\n  [arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n  [arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\ndcr_att_3km = -0.283 (stars=0), dcr_att_3km_se = 0.279\n  [arxiv-2609.27732v1 line 36] ATT | -0.399 | -0.283 | -0.431**\n  [arxiv-2609.27732v1 line 38] | (0.412) | (0.279) | (0.210)\ndcr_obs_5km = 17250\n  [arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\ndcr_narcotics_att_3km = 0.549 (stars=3), dcr_narcotics_att_3km_se = 0.187\n  [arxiv-2609.27732v1 line 82] Narcotics | 1.186*** | 0.549*** | 0.390**\n  [arxiv-2609.27732v1 line 84] | (0.288) | (0.187) | (0.165)\ndcr_fatal_att_1km = -1.207 (stars=3), dcr_fatal_att_1km_se = 0.137\n  [arxiv-2609.27732v1 line 86] Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\n  [arxiv-2609.27732v1 line 88] | (0.137) | (0.215) | (0.178)\ndcr_hiv_att_5km = -0.34 (stars=0), dcr_hiv_att_5km_se = 0.294\n  [arxiv-2609.27732v1 line 78] HIV | 0.063 | -0.239 | -0.340\n  [arxiv-2609.27732v1 line 80] | (0.801) | (0.337) | (0.294)\nlines in arxiv-2609.27732v1 printing an exact p-value (p = ...): []\ndcr_hiv_att_5km_significant_10pct = False (stars on Table 7 HIV 5 km cell: 0)\nri_wage_ate = 1153; printed 95% CI [166, 2141]; p=0.022\n  [arxiv-2606.14621v1 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]\nri_reemployment_ate_pp = 1.47; printed 95% CI [0.32, 2.62]; p=0.013\n  [arxiv-2606.14621v1 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]\nri_weeks_ui_ate = -1.99; printed 95% CI [-2.24, -1.75]; p<0.001\n  [arxiv-2606.14621v1 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]\nri_control_mean_wage = 33043\n  [arxiv-2606.14621v1 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]\nri_wage_ate_se_reported = False (wage ATE cell: '$1,153; p=0.022; 95% CI: [166, 2,141]')\nwrote out/results.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":true},"sandboxed":false,"sourceToolAssistantUUID":"5859de5a-4040-4ec8-a4be-09b9a2ebd6bb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","hash":"527b767f8ab546c5a4aaedf1d7592ca87144ef15d59fc48234a0dbf98fda95e1","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01E9ajCTbxDJxxoAURqL524V","name":"Bash","input":{"command":"mkdir -p out/state; cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-01: extract printed treatment effects/uncertainty from two source extracts.\nReads only data/; writes out/results.json deterministically. No randomness (random_seed = null).\nEvery extracted value is parsed from the text and the matched source line is printed.\"\"\"\nimport json, re, os, hashlib\nfrom pathlib import Path\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\"\nDCR = DATA / \"sources\" / \"arxiv-2609.27732v1.txt\"\nRI = DATA / \"sources\" / \"arxiv-2606.14621v1.txt\"\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for two-sided 95%\n\ndef lines(p):\n    return p.read_text(encoding=\"utf-8\").split(\"\\n\")\n\ndef num(s):\n    \"\"\"Parse a printed number keeping its printed decimals (e.g. '-0.431**' -> -0.431, '$1,153' -> 1153).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"%\", \"\")\n    s = re.sub(r\"\\*+$\", \"\", s).strip()\n    s = s.strip(\"()\")\n    return int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)\n\ndef stars(s):\n    m = re.search(r\"(\\*+)\\s*$\", s.strip()); return len(m.group(1)) if m else 0\n\ndef block(ls, start_pat, end_pat):\n    i0 = next(i for i, l in enumerate(ls) if re.match(start_pat, l))\n    i1 = next(i for i in range(i0 + 1, len(ls)) if re.match(end_pat, ls[i]))\n    return i0, ls[i0:i1]\n\ndef parse_reg_table(ls, title_pat, end_pat):\n    \"\"\"Return header columns, {row: {col: (raw_coef, raw_se, lineno)}}, {row: {col: raw}} for non-coef rows.\"\"\"\n    i0, blk = block(ls, title_pat, end_pat)\n    rows = [(i0 + k + 1, l) for k, l in enumerate(blk) if l.strip() and \" | \" in l or l.startswith(\"|\")]\n    rows = [(n, l) for n, l in rows if l.strip()]\n    header = [c.strip() for c in rows[0][1].split(\"|\")][1:]\n    coef, other = {}, {}\n    k = 1\n    while k < len(rows):\n        n, l = rows[k]\n        cells = [c.strip() for c in l.split(\"|\")]\n        label, vals = cells[0], cells[1:]\n        nxt = rows[k + 1][1] if k + 1 < len(rows) else \"\"\n        if label and nxt.strip().startswith(\"|\") and \"(\" in nxt:\n            ses = [c.strip() for c in nxt.split(\"|\")][1:]\n            coef[label] = {h: (v, s, n, rows[k + 1][0]) for h, v, s in zip(header, vals, ses)}\n            k += 2\n        else:\n            other[label] = {h: (v, n) for h, v in zip(header, vals)}\n            k += 1\n    return header, coef, other\n\ndl = lines(DCR); rl = lines(RI)\nh6, t6, o6 = parse_reg_table(dl, r\"^Table 6:\", r\"^Note:\")\nh7, t7, o7 = parse_reg_table(dl, r\"^Table 7:\", r\"^Note:\")\nhA, tA, oA = parse_reg_table(dl, r\"^Table A19:\", r\"^Note:\")\nif h7 and h7[0] == \"Outcome\":  # Table 7 header line starts with 'Outcome |'\n    pass\n# Table 7 header has 'Outcome' as first cell rather than empty: re-parse header\n_, blk7 = block(dl, r\"^Table 7:\", r\"^Note:\")\nhdr7 = next(l for l in blk7 if l.startswith(\"Outcome |\"))\nh7 = [c.strip() for c in hdr7.split(\"|\")][1:]\n_, t7, o7 = (None, *parse_reg_table(dl, r\"^Table 7:\", r\"^Note:\")[1:])\n# re-key Table 7 entries with the correct header\ndef rekey(tab, hdr):\n    out = {}\n    for r, d in tab.items():\n        vals = list(d.values())\n        out[r] = dict(zip(hdr, vals))\n    return out\nt7 = rekey(t7, h7); o7 = rekey(o7, h7)\n\ndef show(src, n):\n    print(f\"  [{src} line {n}] {(dl if src == DCR_ID else rl)[n - 1]}\")\n\nres, loc = {}, {}\ndef put_coef(key, tab, tabname, row, col):\n    v, s, n1, n2 = tab[row][col]\n    res[key] = num(v); res[key + \"_se\"] = num(s)\n    loc[key] = f'{DCR_ID} | Table {tabname} | row \"{row}\" | column \"{col}\"'\n    loc[key + \"_se\"] = f'{DCR_ID} | Table {tabname} | row \"{row}\" (standard-error line below, in parentheses) | column \"{col}\"'\n    print(f\"{key} = {res[key]} (stars={stars(v)}), {key}_se = {res[key + '_se']}\"); show(DCR_ID, n1); show(DCR_ID, n2)\n    return stars(v)\n\nput_coef(\"dcr_att_5km\", t6, \"6\", \"ATT\", \"5 km\")\nput_coef(\"dcr_att_3km\", t6, \"6\", \"ATT\", \"3 km\")\nv, n = o6[\"Observations\"][\"5 km\"]\nres[\"dcr_obs_5km\"] = num(v)\nloc[\"dcr_obs_5km\"] = f'{DCR_ID} | Table 6 | row \"Observations\" | column \"5 km\"'\nprint(f\"dcr_obs_5km = {res['dcr_obs_5km']}\"); show(DCR_ID, n)\nput_coef(\"dcr_narcotics_att_3km\", t7, \"7\", \"Narcotics\", \"3 km\")\nput_coef(\"dcr_fatal_att_1km\", t7, \"7\", \"Drug-related (fatal)\", \"1 km\")\nhiv_stars = put_coef(\"dcr_hiv_att_5km\", t7, \"7\", \"HIV\", \"5 km\")\n\n# exact p-value: search the whole DCR extract for any printed p-value\npv_lines = [i + 1 for i, l in enumerate(dl) if re.search(r\"\\bp\\s*=\\s*0?\\.\\d\", l)]\nprint(f\"lines in {DCR_ID} printing an exact p-value (p = ...): {pv_lines}\")\nres[\"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 clustered SE \"\n                                  f\"and significance stars; no exact p-value is printed anywhere in the extract)\")\nres[\"dcr_hiv_att_5km_significant_10pct\"] = hiv_stars >= 1\nprint(f\"dcr_hiv_att_5km_significant_10pct = {res['dcr_hiv_att_5km_significant_10pct']} (stars on Table 7 HIV 5 km cell: {hiv_stars})\")\n\n# derived 95% CI from Table 6 printed SE, normal approximation\nb, se = res[\"dcr_att_5km\"], res[\"dcr_att_5km_se\"]\nres[\"dcr_att_5km_ci_low\"] = round(b - Z * se, 4)\nres[\"dcr_att_5km_ci_high\"] = round(b + Z * se, 4)\nloc[\"dcr_att_5km_ci_low\"] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived (estimate - 1.96 x printed SE)'\nloc[\"dcr_att_5km_ci_high\"] = f'{DCR_ID} | Table 6 | row \"ATT\" | column \"5 km\" | derived (estimate + 1.96 x printed SE)'\n\n# ---- RESEA Table 2 ----\n_, blk2 = block(rl, r\"^\\[Table 2\", r\"^Table 2:\")\ni2 = next(i for i, l in enumerate(rl) if l.startswith(\"[Table 2\"))\nhdr = next(l for l in blk2 if l.startswith(\"Outcome |\"))\nH2 = [c.strip() for c in hdr.split(\"|\")]\nT2 = {}\nfor k, l in enumerate(blk2):\n    if \" | \" in l and not l.startswith(\"Outcome |\") and not l.startswith(\"[\"):\n        cells = [c.strip() for c in l.split(\"|\")]\n        T2[cells[0]] = (dict(zip(H2, cells)), i2 + k + 1)\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*([^\\]]+)\\]$\",\n                 cell.replace(\"-$\", \"-\"))\n    if not m:\n        raise ValueError(cell)\n    est = num(m.group(1) + m.group(2))\n    # split CI bounds robustly (thousands separators: \"166, 2,141\")\n    ci = cell.split(\"95% CI:\")[1].strip().strip(\"[]\")\n    parts = re.findall(r\"-?\\d{1,3}(?:,\\d{3})+|-?\\d+(?:\\.\\d+)?\", ci)\n    return est, num(parts[0]), num(parts[1]), m.group(4) + m.group(5)\n\nATE = \"Average treatment effect estimate\"\nfor key, row in [(\"ri_wage_ate\", \"Wage\"), (\"ri_reemployment_ate_pp\", \"Reemployment\"), (\"ri_weeks_ui_ate\", \"Weeks on UI\")]:\n    d, n = T2[row]\n    est, lo, hi, p = parse_effect(d[ATE])\n    res[key] = est\n    loc[key] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (point estimate)'\n    print(f\"{key} = {est}; printed 95% CI [{lo}, {hi}]; p{p}\"); print(f\"  [{RI_ID} line {n}] {rl[n - 1]}\")\n    if key in (\"ri_wage_ate\", \"ri_weeks_ui_ate\"):\n        res[key + \"_ci_low\"], res[key + \"_ci_high\"] = lo, hi\n        loc[key + \"_ci_low\"] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (95% CI lower bound)'\n        loc[key + \"_ci_high\"] = f'{RI_ID} | Table 2 | row \"{row}\" | column \"{ATE}\" (95% CI upper bound)'\nd, n = T2[\"Wage\"]\ncm = re.match(r\"^\\$([\\d,]+)\", d[\"Control\"]).group(1)\nres[\"ri_control_mean_wage\"] = num(cm)\nloc[\"ri_control_mean_wage\"] = f'{RI_ID} | Table 2 | row \"Wage\" | column \"Control\" (mean)'\nprint(f\"ri_control_mean_wage = {res['ri_control_mean_wage']}\"); print(f\"  [{RI_ID} line {n}] {rl[n - 1]}\")\n\nse_in_t2 = bool(re.search(r\"\\bSE\\b|standard error\", \" \".join(blk2), re.I)) and False  # see below\nwage_cell = T2[\"Wage\"][0][ATE]\nres[\"ri_wage_ate_se_reported\"] = bool(re.search(r\"\\(\\s*[\\d,\\.]+\\s*\\)|\\bSE\\b|s\\.e\\.\", wage_cell, re.I))\nprint(f\"ri_wage_ate_se_reported = {res['ri_wage_ate_se_reported']} (wage ATE cell: '{wage_cell}')\")\nlo, hi = res[\"ri_wage_ate_ci_low\"], res[\"ri_wage_ate_ci_high\"]\nres[\"ri_wage_ate_se_derived\"] = round((hi - lo) / (2 * Z), 2)\nloc[\"ri_wage_ate_se_derived\"] = (f'{RI_ID} | Table 2 | row \"Wage\" | column \"{ATE}\" | derived '\n                                 f'((CI upper - CI lower) / (2 x 1.96))')\n\n# ---- consistency checks that feed data_issues ----\nissues = []\nseA = num(tA[\"ATT\"][\"CS-DR\"][1])\nif seA != res[\"dcr_att_5km_se\"]:\n    issues.append({\"issue\": (f\"{DCR_ID}: the same 5 km CS-DR overall ATT ({res['dcr_att_5km']}) is printed with SE \"\n                             f\"{res['dcr_att_5km_se']} in Table 6 but SE {seA} in Appendix Table A19 (column 'CS-DR').\"),\n                   \"handling\": \"Used Table 6 as the key definitions require (dcr_att_5km_se, derived CI); A19 value not used.\"})\nctrl6 = num(o6[\"Control regions\"][\"5 km\"][0])\nmA = re.search(r\"never-treated comparison regions \\((\\d+) regions\\)\", \"\\n\".join(dl))\nif mA and int(mA.group(1)) != ctrl6:\n    issues.append({\"issue\": (f\"{DCR_ID}: Tables 6/7 and A19 rows report {ctrl6} never-treated control regions, but the \"\n                             f\"Table A19 note says {mA.group(1)} never-treated comparison regions.\"),\n                   \"handling\": \"Recorded only; no requested key depends on the control-region count.\"})\nfatal5 = num(t7[\"Drug-related (fatal)\"][\"5 km\"][0])\nissues.append({\"issue\": (f\"{DCR_ID}: the abstract says fatal hospitalisations 'fall by 35%', whereas Table 7 gives a 5 km \"\n                         f\"fatal ATT of {fatal5} log points (exp({fatal5})-1 = {100 * (2.718281828459045 ** fatal5 - 1):.1f}%), \"\n                         f\"and Section 6.1 says 'approximately 30% at 5 km'; the abstract figure appears to equate log points with percent.\"),\n               \"handling\": \"Recorded only; values were taken from the tables named in the key definitions.\"})\nhiv5 = res[\"dcr_hiv_att_5km\"]\nissues.append({\"issue\": (f\"{DCR_ID}: the text describes HIV estimates as 'insignificant and close to zero', but the printed \"\n                         f\"5 km HIV ATT is {hiv5} log points (SE {res['dcr_hiv_att_5km_se']}); it is insignificant (no star) but not numerically close to zero.\"),\n               \"handling\": \"Recorded only; significance flag set from the absence of stars in Table 7.\"})\nissues.append({\"issue\": (f\"{RI_ID}: the Results text states the weeks-on-UI CI as magnitudes '[1.75 weeks, 2.24 weeks]' for a \"\n                         f\"'1.99 week reduction', while Table 2 prints the ATE as -1.99 with CI [-2.24, -1.75].\"),\n               \"handling\": \"Used the signed Table 2 values as required (lower bound first).\"})\nif any(\"¡\" in l for l in rl):\n    issues.append({\"issue\": f\"{RI_ID}: rendering defect in the Section 3 text: 'p ¡ 0.001' (should read p < 0.001).\",\n                   \"handling\": \"Not used for any key (Table 2 cell prints 'p<0.001').\"})\nissues.append({\"issue\": (f\"{RI_ID}: Tables 2 and 3 were re-set by the extract editor into a one-row-per-outcome layout \"\n                         f\"(HTML rendering split rows and detached p-values).\"),\n               \"handling\": \"Relied on the editor's statement that values are verbatim; Table 3 'Difference in means' column matches Table 2 ATE column exactly (checked).\"})\n# check Table 3 duplicates Table 2 ATEs\n_, blk3 = block(rl, r\"^\\[Table 3\", r\"^Table 3:\")\nt3w = next(l for l in blk3 if l.startswith(\"Wage |\")).split(\"|\")[1].strip()\nassert parse_effect(t3w)[:3] == (res[\"ri_wage_ate\"], lo, hi), \"Table 3 vs Table 2 wage mismatch\"\n\ndef wc_l(p):\n    return p.read_bytes().count(b\"\\n\")\n\nout = {\n    \"task_id\": \"F4-conf-01\",\n    **{k: res[k] for k in [\"dcr_att_5km\", \"dcr_att_5km_se\", \"dcr_att_3km\", \"dcr_att_3km_se\", \"dcr_obs_5km\",\n                           \"dcr_narcotics_att_3km\", \"dcr_narcotics_att_3km_se\", \"dcr_fatal_att_1km\", \"dcr_fatal_att_1km_se\",\n                           \"dcr_hiv_att_5km\", \"dcr_hiv_att_5km_se\", \"dcr_hiv_att_5km_p_value\",\n                           \"dcr_hiv_att_5km_significant_10pct\", \"dcr_att_5km_ci_low\", \"dcr_att_5km_ci_high\",\n                           \"ri_wage_ate\", \"ri_wage_ate_ci_low\", \"ri_wage_ate_ci_high\", \"ri_wage_ate_se_reported\",\n                           \"ri_wage_ate_se_derived\", \"ri_control_mean_wage\", \"ri_reemployment_ate_pp\",\n                           \"ri_weeks_ui_ate\", \"ri_weeks_ui_ate_ci_low\", \"ri_weeks_ui_ate_ci_high\"]},\n    \"locators\": loc,\n    \"derivation_notes\": {\n        \"dcr_att_5km_ci\": (\"95% CI = ATT +/- 1.96 x SE using the Table 6 printed ATT (-0.431) and clustered SE (0.210); \"\n                           \"normal (large-sample) critical value 1.96; rounded to 4 decimals. Assumes asymptotic normality of the \"\n                           \"CS-DR estimator and that the printed SE (itself rounded to 3 decimals) is the relevant SE.\"),\n        \"ri_wage_ate_se\": (\"SE = (upper - lower) / (2 x 1.96) using the Table 2 printed 95% CI [166, 2,141]; normal critical value \"\n                           \"1.96 (with N = 23,549 a t critical value is indistinguishable); rounded to 2 decimals. Assumes the \"\n                           \"printed CI is a symmetric Wald interval (midpoint 1153.5 vs estimate 1,153, consistent up to rounding).\"),\n    },\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": issues,\n    \"n_input_rows\": {\"README.md\": wc_l(DATA / \"README.md\"), DCR.name: wc_l(DCR), RI.name: wc_l(RI)},\n    \"n_analysed\": 25,\n    \"random_seed\": None,\n}\nos.makedirs(ROOT / \"out\", exist_ok=True)\n(ROOT / \"out\" / \"results.json\").write_text(json.dumps(out, indent=2, ensure_ascii=False) + \"\\n\", encoding=\"utf-8\")\nprint(\"wrote out/results.json\")\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run the extraction script"}}]}}36{"type":"api-request","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","id":"cea65f5f-b0c4-45d6-9c39-38446ce14a9b","timestamp":"2026-10-30T05:26:44.321Z","version":"2.1.314","querySource":"sdk","shapeHash":"ed2217a4794bcef594a0d666c1d3abc7d8f1f74c3de730245e10e9ba0c4acaf0","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":"9ca45730-bafb-43e0-966e-a42e97f4e49a","keep":8,"tail":["527b767f8ab546c5a4aaedf1d7592ca87144ef15d59fc48234a0dbf98fda95e1","1168da6f382dc8158ae4cfe551d31e95eea148b3c52327490f25cc8d8b2aba03"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c2f04c9cb057213a07530ed1200c71310f3dff9eb4f966198ef03e21cf15cd15"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b87d671e-5c42-40e5-92bf-723ba094895d","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}38{"type":"atis-latch","atis":"","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}39{"parentUuid":"b87d671e-5c42-40e5-92bf-723ba094895d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsFph5Y3sr7bgUEguvP","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":9334,"cache_read_input_tokens":20985,"output_tokens":1267,"output_tokens_details":{"thinking_tokens":226},"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":9334},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2636,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsFpQiUGeWZS4ZxCiHD","requestRef":"cea65f5f-b0c4-45d6-9c39-38446ce14a9b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fce5ac48-b290-4621-8ba1-0d77c3b124a5","timestamp":"2026-10-30T05:26:48.261Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"fce5ac48-b290-4621-8ba1-0d77c3b124a5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsFph5Y3sr7bgUEguvP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EvR3WT71yGUxuUr5a1BDU8","name":"Bash","input":{"command":"sed -i '' '/^se_in_t2 = /d' out/analysis.py\ncd /Users/simon-pierreboucher/wp29/runs/RC0287 && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a 256 out/results.json && cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a 256 out/results.json && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json, math\nfrom scipy.stats import norm\nr=json.load(open(\"out/results.json\"))\ndef p(b,s): return 2*norm.sf(abs(b/s))\nfor k in [\"dcr_att_5km\",\"dcr_att_3km\",\"dcr_narcotics_att_3km\",\"dcr_fatal_att_1km\",\"dcr_hiv_att_5km\"]:\n    print(k, r[k], r[k+\"_se\"], \"z=%.3f p=%.4f\"%(r[k]/r[k+\"_se\"], p(r[k],r[k+\"_se\"])))\nprint(\"CI contains est:\", r[\"dcr_att_5km_ci_low\"]<r[\"dcr_att_5km\"]<r[\"dcr_att_5km_ci_high\"], r[\"dcr_att_5km_ci_low\"], r[\"dcr_att_5km_ci_high\"])\nprint(\"wage CI contains:\", r[\"ri_wage_ate_ci_low\"]<r[\"ri_wage_ate\"]<r[\"ri_wage_ate_ci_high\"], \"mid\", (r[\"ri_wage_ate_ci_low\"]+r[\"ri_wage_ate_ci_high\"])/2, \"se\", r[\"ri_wage_ate_se_derived\"])\nprint(\"wage p implied by derived SE: %.4f (printed 0.022)\"%p(r[\"ri_wage_ate\"], r[\"ri_wage_ate_se_derived\"]))\nprint(\"weeks CI contains:\", r[\"ri_weeks_ui_ate_ci_low\"]<r[\"ri_weeks_ui_ate\"]<r[\"ri_weeks_ui_ate_ci_high\"])\nprint(\"CI excludes 0 for 5km:\", r[\"dcr_att_5km_ci_high\"]<0)\nprint(\"pct change 5km: %.1f%%\"%(100*(math.exp(-0.431)-1)))\nprint(\"locators for all non-boolean keys:\", sorted(set(k for k in r if k.startswith((\"dcr_\",\"ri_\")) and not isinstance(r[k],bool))-set(r[\"locators\"])))\nprint(\"n keys assessed:\", len([k for k in r if k.startswith((\"dcr_\",\"ri_\"))]))\nprint(r[\"n_input_rows\"])\nEOF\nwc -l data/README.md data/sources/*","description":"Reproducibility and consistency 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 out/results.json\n709edfbe33ed363ee5153f7f99a001195dea4ff567bc7b19d37d63026b753683  out/results.json\nIDENTICAL\ndcr_att_5km -0.431 0.21 z=-2.052 p=0.0401\ndcr_att_3km -0.283 0.279 z=-1.014 p=0.3104\ndcr_narcotics_att_3km 0.549 0.187 z=2.936 p=0.0033\ndcr_fatal_att_1km -1.207 0.137 z=-8.810 p=0.0000\ndcr_hiv_att_5km -0.34 0.294 z=-1.156 p=0.2475\nCI contains est: True -0.8426 -0.0194\nwage CI contains: True mid 1153.5 se 503.83\nwage p implied by derived SE: 0.0221 (printed 0.022)\nweeks CI contains: True\nCI excludes 0 for 5km: True\npct change 5km: -35.0%\nlocators for all non-boolean keys: []\nn keys assessed: 25\n{'README.md': 10, 'arxiv-2609.27732v1.txt': 134, 'arxiv-2606.14621v1.txt': 58}\n      10 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     202 total","is_error":false}]},"uuid":"6def9fc3-8939-405b-a5c4-74cc89186f7f","timestamp":"2026-10-30T05:26:56.672Z","toolUseResult":{"stdout":"709edfbe33ed363ee5153f7f99a001195dea4ff567bc7b19d37d63026b753683  out/results.json\n709edfbe33ed363ee5153f7f99a001195dea4ff567bc7b19d37d63026b753683  out/results.json\nIDENTICAL\ndcr_att_5km -0.431 0.21 z=-2.052 p=0.0401\ndcr_att_3km -0.283 0.279 z=-1.014 p=0.3104\ndcr_narcotics_att_3km 0.549 0.187 z=2.936 p=0.0033\ndcr_fatal_att_1km -1.207 0.137 z=-8.810 p=0.0000\ndcr_hiv_att_5km -0.34 0.294 z=-1.156 p=0.2475\nCI contains est: True -0.8426 -0.0194\nwage CI contains: True mid 1153.5 se 503.83\nwage p implied by derived SE: 0.0221 (printed 0.022)\nweeks CI contains: True\nCI excludes 0 for 5km: True\npct change 5km: -35.0%\nlocators for all non-boolean keys: []\nn keys assessed: 25\n{'README.md': 10, 'arxiv-2609.27732v1.txt': 134, 'arxiv-2606.14621v1.txt': 58}\n      10 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     202 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-i '' '/^se_in_t2 = /d' out/analysis.py\ncd /Users/simon-pierreboucher/wp29/runs/RC0287 && rm out/results.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a 256 out/results.json && cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && shasum -a 256 out/results.json && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json, math\nfrom scipy.stats import norm\nr=json.load(open(\"out/results.json\"))\ndef p(b,s): return 2*norm.sf(abs(b/s))\nfor k in [\"dcr_att_5km\",\"dcr_att_3km\",\"dcr_narcotics_att_3km\",\"dcr_fatal_att_1km\",\"dcr_hiv_att_5km\"]:\n    print(k, r[k], r[k+\"_se\"], \"z=%.3f p=%.4f\"%(r[k]/r[k+\"_se\"], p(r[k],r[k+\"_se\"])))\nprint(\"CI contains est:\", r[\"dcr_att_5km_ci_low\"]<r[\"dcr_att_5km\"]<r[\"dcr_att_5km_ci_high\"], r[\"dcr_att_5km_ci_low\"], r[\"dcr_att_5km_ci_high\"])\nprint(\"wage CI contains:\", r[\"ri_wage_ate_ci_low\"]<r[\"ri_wage_ate\"]<r[\"ri_wage_ate_ci_high\"], \"mid\", (r[\"ri_wage_ate_ci_low\"]+r[\"ri_wage_ate_ci_high\"])/2, \"se\", r[\"ri_wage_ate_se_derived\"])\nprint(\"wage p implied by derived SE: %.4f (printed 0.022)\"%p(r[\"ri_wage_ate\"], r[\"ri_wage_ate_se_derived\"]))\nprint(\"weeks CI contains:\", r[\"ri_weeks_ui_ate_ci_low\"]<r[\"ri_weeks_ui_ate\"]<r[\"ri_weeks_ui_ate_ci_high\"])\nprint(\"CI excludes 0 for 5km:\", r[\"dcr_att_5km_ci_high\"]<0)\nprint(\"pct change 5km: %.1f%%\"%(100*(math.exp(-0.431)-1)))\nprint(\"locators for all non-boolean keys:\", sorted(set(k for k in r if k.startswith((\"dcr_\",\"ri_\")) and not isinstance(r[k],bool))-set(r[\"locators\"])))\nprint(\"n keys assessed:\", len([k for k in r if k.startswith((\"dcr_\",\"ri_\"))]))\nprint(r[\"n_input_rows\"])\nEOF\nwc -l data/README.md data/sources/*","description":"Reproducibility and consistency checks"}}]}}43{"type":"api-request","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","id":"9a320971-736a-4dc4-bfd7-59d35f187335","timestamp":"2026-10-30T05:26:56.676Z","version":"2.1.314","querySource":"sdk","shapeHash":"ed2217a4794bcef594a0d666c1d3abc7d8f1f74c3de730245e10e9ba0c4acaf0","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":"cea65f5f-b0c4-45d6-9c39-38446ce14a9b","keep":10,"tail":["13603df2c8d9d69f03ad2410eae5a1e2a227d51bb73521e51ee9ca53eeeb05e3","1f90b90fd18040d1ab7373b424e54607dc0efd32706bcec789cd6bebb42bb836"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6c2e02475580271cea2a041303684406a223441903fe2a466c2f31ef7fe7a71a"}44{"parentUuid":"6def9fc3-8939-405b-a5c4-74cc89186f7f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsGjX4hxB4LciGDqPFa","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":1781,"cache_read_input_tokens":30319,"output_tokens":2515,"output_tokens_details":{"thinking_tokens":87},"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":1781},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1443,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsGjEhLE1PGoUwJdsoo","requestRef":"9a320971-736a-4dc4-bfd7-59d35f187335","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ba553fcc-f908-41e3-8e8a-952a78cb0224","timestamp":"2026-10-30T05:26:59.445Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"ba553fcc-f908-41e3-8e8a-952a78cb0224","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsGjX4hxB4LciGDqPFa","type":"message","role":"assistant","content":[{"type":"text","text":"All checks pass. Next I'll write the state files, the report and the claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1781,"cache_read_input_tokens":30319,"output_tokens":2515,"output_tokens_details":{"thinking_tokens":87},"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":1781},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsGjEhLE1PGoUwJdsoo","requestRef":"9a320971-736a-4dc4-bfd7-59d35f187335","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"35213af3-1b58-4a65-8077-684d86ae448c","timestamp":"2026-10-30T05:26:59.445Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"35213af3-1b58-4a65-8077-684d86ae448c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsGjX4hxB4LciGDqPFa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018DGc3cnNEAeHN4nfikLzph","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0287/out\ncat > state/contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Pull out specified treatment effects, their uncertainty and sample counts from two published results tables, exactly as printed: Armendariz (2026) DCR staggered DiD, Tables 6/7, in log points with clustered SEs; Li et al. (2026) RESEA RCT, Table 2, ATEs with 95% CIs. Derive (a) a 95% CI from a printed SE (DCR Table 6 5 km) and (b) an SE from a printed 95% CI (RESEA wage ATE), stating the convention. Spot the requested quantity that is not printed (the exact p-value of the HIV 5 km ATT).\n**Estimands.** The printed numbers themselves. The derived quantities only make sense if the estimators are approximately normal (Wald-type intervals) and the CI is symmetric.\n**Outputs.** out/results.json (25 dcr_/ri_ keys + locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Information rules.** Only the two extracts count as evidence. Values are taken from the table named in each key. Values keep the printed sign and decimals. Locator format is `<source> | Table n | row \"..\" | column \"..\"`. Derived values name their input table and say \"derived\". A quantity that is not printed is reported as null and the locator says so.\n**Invalid if:** values come from memory or from another table (e.g. A19 instead of Table 6), numbers are rescaled, or a p-value is invented.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | yes (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 with \" | \" separators. In DCR regression tables the SE line follows each coefficient row. DCR units are log points; the controls are 608 never-treated MedStat regions; the panel covers 1998-2022 openings. RESEA Table 2 cells hold \"estimate; p; 95% CI\"; wages are USD/yr, reemployment is pp and UI duration is weeks; the trial ran Feb 2022 to Sep 2023 with N=23,549.\nMissing: no exact p-values anywhere in the DCR extract (stars only). No SEs are printed in RESEA Table 2.\nAnomalies (also in results.json data_issues):\n1. Table 6 5 km SE (0.210) differs from A19 CS-DR SE (0.218) for the same estimate.\n2. 608 vs 640 control regions (A19 note).\n3. The abstract says fatal hospitalisations fall \"35%\", but Table 7 gives -0.354 log points (about -29.8%; Sec 6.1 says ~30%).\n4. The text calls the HIV estimates \"close to zero\", but the 5 km value is -0.340.\n5. The RESEA text gives the weeks CI as unsigned magnitudes.\n6. \"p ¡ 0.001\" rendering defect.\n7. RESEA tables were re-set by the editor.\nAvailability: neither file breaks the rules. No external information is used.\nEOF\ncat > exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"No filtering: all 25 requested items extracted from the named tables\",25,25,0,\"Extraction task; nothing excluded. Table A19 SE and text-stated values were not used because the key definitions name Table 6/7/2\"\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n- Reproducibility: deleted out/results.json, ran analysis.py twice; SHA-256 identical both times (709edfbe…3683). PASS\n- Printed source lines echoed by the script match every extracted value (see stdout). PASS\n- Stars vs printed SE (normal z): 5 km ATT z=-2.05, p≈0.040 → ** OK; 3 km z=-1.01 → no star OK; narcotics 3 km z=2.94, p≈0.003 → *** OK; fatal 1 km z=-8.8 → *** OK; HIV 5 km z=-1.16, p≈0.25 → no star OK (and significant_10pct=false). PASS\n- The derived DCR 95% CI [-0.8426, -0.0194] contains -0.431 and excludes 0, consistent with ** (p<0.05). PASS\n- RESEA wage CI [166, 2141] contains 1153; midpoint 1153.5 ≈ estimate (symmetric). The derived SE of 503.83 gives p=0.0221 vs the printed 0.022. PASS\n- Weeks CI [-2.24, -1.75] contains -1.99, and the lower bound comes first. PASS\n- SEs positive; dcr_obs_5km integer 17250. PASS\n- Table 3 \"Difference in means\" wage cell equals the Table 2 ATE cell (assert in script). PASS\n- Every non-boolean key has a locator; n_input_rows equals wc -l (10/134/58). PASS\n- Note: JSON stores 0.210 as 0.21 and -0.340 as -0.34 (the numeric value is the same; the printed decimals are recorded in the locators/report). Not a failure.\nEOF\ncat > state/log.md <<'EOF'\n- 2026-10-30 P0: read TASK.md; contract written.\n- 2026-10-30 P1: hashes match README; 7 data issues logged; no exclusions.\n- 2026-10-30 P2: baseline = direct reading of printed cells (no estimation in task); no separate baseline_* keys needed since the task names all keys.\n- 2026-10-30 P3: analysis.py parses tables, echoes source lines; decisions: Table 6 SE (not A19), z=1.96 for both derivations, HIV p-value null.\n- 2026-10-30 P4: rerun identical; all consistency checks pass.\n- 2026-10-30 P5: report and claims written.\nEOF","description":"Write contract, audit, exclusions, checks, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1781,"cache_read_input_tokens":30319,"output_tokens":2515,"output_tokens_details":{"thinking_tokens":87},"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":1781},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsGjEhLE1PGoUwJdsoo","requestRef":"9a320971-736a-4dc4-bfd7-59d35f187335","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7ae2c755-da86-445a-bb9d-46f3e681d535","timestamp":"2026-10-30T05:27:21.185Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"7ae2c755-da86-445a-bb9d-46f3e681d535","isSidechain":false,"promptId":"82d16e65-fda4-438c-8139-8fd74dfb1c5b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018DGc3cnNEAeHN4nfikLzph","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"5810bdb0-c1d8-4cfe-b4e1-d7e74ebc3fb3","timestamp":"2026-10-30T05:27:21.215Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7ae2c755-da86-445a-bb9d-46f3e681d535","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287/out","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}48{"type":"api-request-blob","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","hash":"0a22d664c9dda43e88fe8da99272fd7e327d9e23a639b61bcf7129b75d634129","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS4AcKEAgTGAI4AUIIdGhpbmtpbmcSDHPyusWQLdiG5nq1eBoMm8wZCpquhdOPxdCtIjDXXQyeFVXJtZZK+4n3naP4iGEtkrUtKQn7vIVTKhDBzu9IQsEXfinHekPVOz0f0wQq/QbuN27U0g240K9gkc9pBjS3rh3KAXdwCgcAPgPuilAIkwVr79DkNjYpyOZ4GkUdVcud6f6bOk0uOupup8vLM1maRStaTILqeo8B6159y/NkBVOmBRqnSBQuSARCsd0TshZvryncgpWP2hueuS1an2Dq1moSE9ZPg0nGqzNPmOjBb9OXfDrFD1F7fDucDmKTCcn3yGUtcz2bwRIEaPiUbZikcAhbhsCUWDAqigXQdDwZ2QdxzBPbiEWwqhH6jZQjeDZ4B7eh5YLyPs90onoUwT6n+n/J8TSNkaomwURFnVgSVk5a0YVs5ZSvYANtqYhqLT8YOb40r5HU3Zpged7BcBAIYTnFXb63AbtHtjnt0ujUMdZ59iGut8/GfSxeB5FaWlzUEFocFcTHwHLdngMt2iVT+ncmcjZke5B8iU5Os836q8DCnqKFpXu89wUf2c/sArLXjPnTuir/+x9Tm+UN6A4S/9xE69Qq6XCYVueLGkKO/r3sX6dM5JQnwIU69DYUw4aZfyoNauQE3pFUWwWTaZJUcHnAPeBOjzECXayIEE9WXiiAwoXfjitPhYDV04vuumthhnjdu7Y+q6Ctk7u7xw1pY9hxDF0B/wn/SL7gBlsrTzXbBMnF8b1eP8uDPiHHsKvAdNtmTh1OihAAM6CsS/YgsYeQzn9nG1jYKcVxiLckeq4gtraQumdrlRGKZ4am0eA0JXUTXJZy7t+R5YcU9zrJB6e4+2BwEy4TXMpYKwLwKsm5ex9E14SyxxseZ4Nkg6S2s/okqMPHKMofhGPrbvXLKoZ0FtPDC9LMiMcxAzmu3cPk2oGzhFY1ysHIbraL5itBicHglFeBtBZ1hVCpC7GVlwQSHmZqBiW4dmJciEc4mmaphyrkJCvR4nlGbI2adNCuEnmnSM7FW8l7bDUdtnI6gQWPe+47cn0iP7yjwgxTVdryjTKfMXFeR9E9zA+0WKtUx4CIhi3ccxwRJm0drleQaJjfEJU+LnBc6NxfvxpdsOk7b1cmIJxKSLjFrudJ/ieelgEBXyorvlJmdJhfopnibF4us8hIrAaTdHoQFo+K7FhSYdp5+OM8pdObRDYNegpXVrqblM6J7TURrfQK975fMs5MiUZGYkNOLbjldJVe51RXRGtL9IC97qdn1B9K6b/oUf5lfUC6vju7IyyQfJaelnBDOh/Z3D0/9LQ2iBgB"},{"type":"text","text":"All checks pass. Next I'll write the state files, the report and the claims."},{"type":"tool_use","id":"toolu_018DGc3cnNEAeHN4nfikLzph","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0287/out\ncat > state/contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Pull out specified treatment effects, their uncertainty and sample counts from two published results tables, exactly as printed: Armendariz (2026) DCR staggered DiD, Tables 6/7, in log points with clustered SEs; Li et al. (2026) RESEA RCT, Table 2, ATEs with 95% CIs. Derive (a) a 95% CI from a printed SE (DCR Table 6 5 km) and (b) an SE from a printed 95% CI (RESEA wage ATE), stating the convention. Spot the requested quantity that is not printed (the exact p-value of the HIV 5 km ATT).\n**Estimands.** The printed numbers themselves. The derived quantities only make sense if the estimators are approximately normal (Wald-type intervals) and the CI is symmetric.\n**Outputs.** out/results.json (25 dcr_/ri_ keys + locators, derivation_notes, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed), out/report.md (6 sections), out/analysis.py, out/claims.json.\n**Information rules.** Only the two extracts count as evidence. Values are taken from the table named in each key. Values keep the printed sign and decimals. Locator format is `<source> | Table n | row \"..\" | column \"..\"`. Derived values name their input table and say \"derived\". A quantity that is not printed is reported as null and the locator says so.\n**Invalid if:** values come from memory or from another table (e.g. A19 instead of Table 6), numbers are rescaled, or a p-value is invented.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | lines (wc -l) | matches README/bundle hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | yes (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 with \" | \" separators. In DCR regression tables the SE line follows each coefficient row. DCR units are log points; the controls are 608 never-treated MedStat regions; the panel covers 1998-2022 openings. RESEA Table 2 cells hold \"estimate; p; 95% CI\"; wages are USD/yr, reemployment is pp and UI duration is weeks; the trial ran Feb 2022 to Sep 2023 with N=23,549.\nMissing: no exact p-values anywhere in the DCR extract (stars only). No SEs are printed in RESEA Table 2.\nAnomalies (also in results.json data_issues):\n1. Table 6 5 km SE (0.210) differs from A19 CS-DR SE (0.218) for the same estimate.\n2. 608 vs 640 control regions (A19 note).\n3. The abstract says fatal hospitalisations fall \"35%\", but Table 7 gives -0.354 log points (about -29.8%; Sec 6.1 says ~30%).\n4. The text calls the HIV estimates \"close to zero\", but the 5 km value is -0.340.\n5. The RESEA text gives the weeks CI as unsigned magnitudes.\n6. \"p ¡ 0.001\" rendering defect.\n7. RESEA tables were re-set by the editor.\nAvailability: neither file breaks the rules. No external information is used.\nEOF\ncat > exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"No filtering: all 25 requested items extracted from the named tables\",25,25,0,\"Extraction task; nothing excluded. Table A19 SE and text-stated values were not used because the key definitions name Table 6/7/2\"\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n- Reproducibility: deleted out/results.json, ran analysis.py twice; SHA-256 identical both times (709edfbe…3683). PASS\n- Printed source lines echoed by the script match every extracted value (see stdout). PASS\n- Stars vs printed SE (normal z): 5 km ATT z=-2.05, p≈0.040 → ** OK; 3 km z=-1.01 → no star OK; narcotics 3 km z=2.94, p≈0.003 → *** OK; fatal 1 km z=-8.8 → *** OK; HIV 5 km z=-1.16, p≈0.25 → no star OK (and significant_10pct=false). PASS\n- The derived DCR 95% CI [-0.8426, -0.0194] contains -0.431 and excludes 0, consistent with ** (p<0.05). PASS\n- RESEA wage CI [166, 2141] contains 1153; midpoint 1153.5 ≈ estimate (symmetric). The derived SE of 503.83 gives p=0.0221 vs the printed 0.022. PASS\n- Weeks CI [-2.24, -1.75] contains -1.99, and the lower bound comes first. PASS\n- SEs positive; dcr_obs_5km integer 17250. PASS\n- Table 3 \"Difference in means\" wage cell equals the Table 2 ATE cell (assert in script). PASS\n- Every non-boolean key has a locator; n_input_rows equals wc -l (10/134/58). PASS\n- Note: JSON stores 0.210 as 0.21 and -0.340 as -0.34 (the numeric value is the same; the printed decimals are recorded in the locators/report). Not a failure.\nEOF\ncat > state/log.md <<'EOF'\n- 2026-10-30 P0: read TASK.md; contract written.\n- 2026-10-30 P1: hashes match README; 7 data issues logged; no exclusions.\n- 2026-10-30 P2: baseline = direct reading of printed cells (no estimation in task); no separate baseline_* keys needed since the task names all keys.\n- 2026-10-30 P3: analysis.py parses tables, echoes source lines; decisions: Table 6 SE (not A19), z=1.96 for both derivations, HIV p-value null.\n- 2026-10-30 P4: rerun identical; all consistency checks pass.\n- 2026-10-30 P5: report and claims written.\nEOF","description":"Write contract, audit, exclusions, checks, log"}}]}}49{"type":"api-request","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","id":"fd7e2a47-0f03-43a4-a3c6-19c8efaa22e0","timestamp":"2026-10-30T05:27:21.218Z","version":"2.1.314","querySource":"sdk","shapeHash":"ed2217a4794bcef594a0d666c1d3abc7d8f1f74c3de730245e10e9ba0c4acaf0","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":"9a320971-736a-4dc4-bfd7-59d35f187335","keep":12,"tail":["0a22d664c9dda43e88fe8da99272fd7e327d9e23a639b61bcf7129b75d634129","60d16bcbbe8eb8aa346eac935bf6e1fe89640380353207af51e7d907c29dfc33"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5994cc924bce877fdcf0110bccd1284fcce4467860d5ae27ab09bd02b27235f5"}50{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5810bdb0-c1d8-4cfe-b4e1-d7e74ebc3fb3","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}51{"type":"atis-latch","atis":"","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc"}52{"parentUuid":"5810bdb0-c1d8-4cfe-b4e1-d7e74ebc3fb3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsJYTreaMvUxsn3x3sv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xsqe8BbMPN4KQaoxFExwy9","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0287/out\ncat > report.md <<'EOF'\n# F4-conf-01 — Extracted treatment effects and uncertainty\n\n## Question\nThis task pulls printed effect estimates, uncertainty measures and counts from two open-access papers, exactly as printed. The first is Armendariz (2026), arXiv:2609.27732v1, which studies drug consumption rooms (DCR) with a staggered difference-in-differences design. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, a randomised trial of Rhode Island's RESEA programme. The task also derives one CI from an SE and one SE from a CI, and flags a quantity the source does not report. Nothing is re-estimated.\n\n## Data and exclusions\n- Inputs: two source extracts and `data/README.md`. The SHA-256 hashes match the README and bundle. Line counts are in `n_input_rows`: 134, 58 and 10 lines.\n- No items were excluded (`out/exclusions.csv`). There were 25 requested items, all assessed (`n_analysed`).\n- Each value comes from the table named in its key, even when a different table prints a conflicting value (see Limitations).\n\n## Method\n`out/analysis.py` parses the \" | \"-separated tables, converts printed strings to numbers without rounding, and prints the matched source lines. It writes `out/results.json` deterministically; no randomness is involved (`random_seed` = null). Each key has a locator in `locators`.\n\nDerivation conventions (from `derivation_notes`):\n- **CI from SE:** estimate ± 1.96 × SE (normal approximation).\n- **SE from CI:** (upper − lower)/(2 × 1.96).\n\n## Results\n**DCR study, Table 6 (log points; clustered SE in parentheses):**\n- 5 km ATT: −0.431** with SE 0.210 (`dcr_att_5km`, `dcr_att_5km_se`), based on 17,250 observations (`dcr_obs_5km`).\n- 3 km ATT: −0.283 with SE 0.279 (`dcr_att_3km`, `dcr_att_3km_se`).\n- Derived 95% CI for the 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**DCR study, Table 7:**\n- Narcotics, 3 km: 0.549*** with SE 0.187 (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`).\n- Drug-related (fatal), 1 km: −1.207*** with SE 0.137 (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`).\n- HIV, 5 km: −0.340 with SE 0.294 and no star, so it is not significant at 10% (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`, `dcr_hiv_att_5km_significant_10pct` = false).\n- The exact p-value for HIV at 5 km is **not reported**. The extract prints only stars, so `dcr_hiv_att_5km_p_value` = null.\n\n**RESEA study, Table 2:**\n- Wage ATE: $1,153 per year, 95% CI [166, 2,141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`). Control-group mean wage: $33,043 (`ri_control_mean_wage`).\n- The source prints no SE for the wage ATE (`ri_wage_ate_se_reported` = false). The derived SE is 503.83 (`ri_wage_ate_se_derived`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks on UI ATE: −1.99, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nJSON drops trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34. The numeric values are unchanged, and the printed forms are given above.\n\n## Uncertainty\n**What each source reports:**\n- **DCR, Tables 6 and 7:** standard errors clustered at the MedStat region level, plus stars for p<0.01, p<0.05 and p<0.10. The extract contains no confidence intervals and no exact p-values for these cells.\n- **RESEA, Table 2:** two-sided p-values and 95% confidence intervals from the authors' pre-registered methodology. No standard errors are printed.\n\n**What the derived values assume:**\n- **DCR 5 km CI:** the estimator is asymptotically normal, the printed SE (rounded to 3 decimals) is the correct one, and z = 1.96. A t critical value or 2.0 would widen the interval slightly.\n- **RESEA wage SE:** the printed CI is a symmetric Wald interval with z = 1.96. The midpoint is 1153.5, matching the estimate up to rounding. With N = 23,549 a t critical value would make no practical difference. Converting back, this SE gives p ≈ 0.022, the same as the printed p (`out/state/checks.md`).\n- **Rounding:** the derived values carry the rounding error of the printed inputs.\n\n## Limitations and open issues\n- **Requested value not printed:** the exact p-value for the HIV 5 km ATT is not printed, so it is null.\n- **Problems found in the sources** (all listed in `data_issues`):\n  1. The same 5 km CS-DR estimate has SE 0.210 in Table 6 but 0.218 in Table A19. I used Table 6, as the key requires.\n  2. The Table A19 note gives 640 control regions; the tables give 608.\n  3. The abstract says fatal hospitalisations fall by \"35%\", but the Table 7 5 km value of −0.354 log points is about −29.8% (Section 6.1 says ~30%).\n  4. The text calls the HIV estimates \"close to zero\", but the 5 km value is −0.340.\n  5. The RESEA text gives the weeks-on-UI CI as unsigned magnitudes; Table 2 is signed.\n  6. The RESEA text contains a rendering defect, \"p ¡ 0.001\".\n  7. The RESEA tables were re-set by the extract editor. I relied on the editor's statement that the values are verbatim; Table 3's first column matches Table 2.\n- **Overall:** feasibility is \"complete\" (`feasibility`). The null p-value is the expected answer for a quantity the source does not report, not a failure.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"DCR Table 6 prints a 5 km ATT of -0.431** (SE 0.210) and 17,250 observations; 3 km ATT -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 estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Derived 95% CI for the 5 km ATT is [-0.8426, -0.0194] using estimate +/- 1.96 x SE.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"Assumes normality; with the A19 SE it would be wider.\"},\n {\"id\":\"C3\",\"text\":\"DCR Table 7 prints Narcotics 3 km 0.549*** (0.187), Drug-related (fatal) 1 km -1.207*** (0.137), 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\":\"1 km estimates rest on one treated region per cohort (source note).\"},\n {\"id\":\"C4\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the source; the cell carries no star, so it is not significant at 10%.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"Only significance stars are printed.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141], no standard error, and a control mean of $33,043; derived SE = 503.83 (z = 1.96).\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric Wald interval.\"},\n {\"id\":\"C6\",\"text\":\"RESEA Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Text states the weeks CI as unsigned magnitudes; signed table values used.\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (Table 6 vs A19 SE; 608 vs 640 controls; abstract 35% vs table-implied ~30% for fatal outcomes) recorded in data_issues.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Recorded only; they do not change any extracted key.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read in full\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 inputs match README/bundle\",\"wc -l recorded (10/134/58)\",\"exclusion ledger: 25->25, 0 removed\",\"7 data issues documented\"],\"notes\":\"No availability-rule violations\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline = direct reading of printed cells; matched source lines echoed\"],\"notes\":\"Task names all keys; no baseline_* keys needed\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py parses data/ only and writes results.json\",\"no randomness; random_seed null\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun x2: identical SHA-256\",\"stars consistent with z from printed SE for all 5 cells\",\"derived CI contains estimate\",\"wage CI symmetric; derived SE reproduces p=0.022\",\"locators complete; n_input_rows equal wc -l\"],\"notes\":\"see state/checks.md\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections citing keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'))\nprint([k for cl in c for k in cl['results_keys'] if k not in r]); json.load(open('state/phases.json')); print('ok')\"; ls -R","description":"Write report, claims, phase ledger; validate keys"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2530,"cache_read_input_tokens":32100,"output_tokens":4122,"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":2530},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsJYAzS2j9NKoVrJ23b","requestRef":"fd7e2a47-0f03-43a4-a3c6-19c8efaa22e0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"306a50b1-7b48-4d64-ad6b-6d6568100b11","timestamp":"2026-10-30T05:27:54.454Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287/out","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"306a50b1-7b48-4d64-ad6b-6d6568100b11","isSidechain":false,"promptId":"82d16e65-fda4-438c-8139-8fd74dfb1c5b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Xsqe8BbMPN4KQaoxFExwy9","type":"tool_result","content":"[]\nok\nanalysis.py\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate\n\n./state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","is_error":false}]},"uuid":"c7cd67bd-34d9-408e-8348-c76ac13e63fc","timestamp":"2026-10-30T05:27:54.493Z","toolUseResult":{"stdout":"[]\nok\nanalysis.py\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nstate\n\n./state:\nchecks.md\ncontract.md\ndata_audit.md\nlog.md\nphases.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"306a50b1-7b48-4d64-ad6b-6d6568100b11","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287/out","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","hash":"bb1774cbda0119fac172f5007569274c326b5b3b968a45bbfc04efa274599947","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Xsqe8BbMPN4KQaoxFExwy9","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0287/out\ncat > report.md <<'EOF'\n# F4-conf-01 — Extracted treatment effects and uncertainty\n\n## Question\nThis task pulls printed effect estimates, uncertainty measures and counts from two open-access papers, exactly as printed. The first is Armendariz (2026), arXiv:2609.27732v1, which studies drug consumption rooms (DCR) with a staggered difference-in-differences design. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, a randomised trial of Rhode Island's RESEA programme. The task also derives one CI from an SE and one SE from a CI, and flags a quantity the source does not report. Nothing is re-estimated.\n\n## Data and exclusions\n- Inputs: two source extracts and `data/README.md`. The SHA-256 hashes match the README and bundle. Line counts are in `n_input_rows`: 134, 58 and 10 lines.\n- No items were excluded (`out/exclusions.csv`). There were 25 requested items, all assessed (`n_analysed`).\n- Each value comes from the table named in its key, even when a different table prints a conflicting value (see Limitations).\n\n## Method\n`out/analysis.py` parses the \" | \"-separated tables, converts printed strings to numbers without rounding, and prints the matched source lines. It writes `out/results.json` deterministically; no randomness is involved (`random_seed` = null). Each key has a locator in `locators`.\n\nDerivation conventions (from `derivation_notes`):\n- **CI from SE:** estimate ± 1.96 × SE (normal approximation).\n- **SE from CI:** (upper − lower)/(2 × 1.96).\n\n## Results\n**DCR study, Table 6 (log points; clustered SE in parentheses):**\n- 5 km ATT: −0.431** with SE 0.210 (`dcr_att_5km`, `dcr_att_5km_se`), based on 17,250 observations (`dcr_obs_5km`).\n- 3 km ATT: −0.283 with SE 0.279 (`dcr_att_3km`, `dcr_att_3km_se`).\n- Derived 95% CI for the 5 km ATT: [−0.8426, −0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**DCR study, Table 7:**\n- Narcotics, 3 km: 0.549*** with SE 0.187 (`dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se`).\n- Drug-related (fatal), 1 km: −1.207*** with SE 0.137 (`dcr_fatal_att_1km`, `dcr_fatal_att_1km_se`).\n- HIV, 5 km: −0.340 with SE 0.294 and no star, so it is not significant at 10% (`dcr_hiv_att_5km`, `dcr_hiv_att_5km_se`, `dcr_hiv_att_5km_significant_10pct` = false).\n- The exact p-value for HIV at 5 km is **not reported**. The extract prints only stars, so `dcr_hiv_att_5km_p_value` = null.\n\n**RESEA study, Table 2:**\n- Wage ATE: $1,153 per year, 95% CI [166, 2,141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`). Control-group mean wage: $33,043 (`ri_control_mean_wage`).\n- The source prints no SE for the wage ATE (`ri_wage_ate_se_reported` = false). The derived SE is 503.83 (`ri_wage_ate_se_derived`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks on UI ATE: −1.99, 95% CI [−2.24, −1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nJSON drops trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34. The numeric values are unchanged, and the printed forms are given above.\n\n## Uncertainty\n**What each source reports:**\n- **DCR, Tables 6 and 7:** standard errors clustered at the MedStat region level, plus stars for p<0.01, p<0.05 and p<0.10. The extract contains no confidence intervals and no exact p-values for these cells.\n- **RESEA, Table 2:** two-sided p-values and 95% confidence intervals from the authors' pre-registered methodology. No standard errors are printed.\n\n**What the derived values assume:**\n- **DCR 5 km CI:** the estimator is asymptotically normal, the printed SE (rounded to 3 decimals) is the correct one, and z = 1.96. A t critical value or 2.0 would widen the interval slightly.\n- **RESEA wage SE:** the printed CI is a symmetric Wald interval with z = 1.96. The midpoint is 1153.5, matching the estimate up to rounding. With N = 23,549 a t critical value would make no practical difference. Converting back, this SE gives p ≈ 0.022, the same as the printed p (`out/state/checks.md`).\n- **Rounding:** the derived values carry the rounding error of the printed inputs.\n\n## Limitations and open issues\n- **Requested value not printed:** the exact p-value for the HIV 5 km ATT is not printed, so it is null.\n- **Problems found in the sources** (all listed in `data_issues`):\n  1. The same 5 km CS-DR estimate has SE 0.210 in Table 6 but 0.218 in Table A19. I used Table 6, as the key requires.\n  2. The Table A19 note gives 640 control regions; the tables give 608.\n  3. The abstract says fatal hospitalisations fall by \"35%\", but the Table 7 5 km value of −0.354 log points is about −29.8% (Section 6.1 says ~30%).\n  4. The text calls the HIV estimates \"close to zero\", but the 5 km value is −0.340.\n  5. The RESEA text gives the weeks-on-UI CI as unsigned magnitudes; Table 2 is signed.\n  6. The RESEA text contains a rendering defect, \"p ¡ 0.001\".\n  7. The RESEA tables were re-set by the extract editor. I relied on the editor's statement that the values are verbatim; Table 3's first column matches Table 2.\n- **Overall:** feasibility is \"complete\" (`feasibility`). The null p-value is the expected answer for a quantity the source does not report, not a failure.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"DCR Table 6 prints a 5 km ATT of -0.431** (SE 0.210) and 17,250 observations; 3 km ATT -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 estimate; Table 6 used per key definition.\"},\n {\"id\":\"C2\",\"text\":\"Derived 95% CI for the 5 km ATT is [-0.8426, -0.0194] using estimate +/- 1.96 x SE.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\"],\"status\":\"supported\",\"caveat\":\"Assumes normality; with the A19 SE it would be wider.\"},\n {\"id\":\"C3\",\"text\":\"DCR Table 7 prints Narcotics 3 km 0.549*** (0.187), Drug-related (fatal) 1 km -1.207*** (0.137), 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\":\"1 km estimates rest on one treated region per cohort (source note).\"},\n {\"id\":\"C4\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in the source; the cell carries no star, so it is not significant at 10%.\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"Only significance stars are printed.\"},\n {\"id\":\"C5\",\"text\":\"RESEA Table 2 prints a wage ATE of $1,153 with 95% CI [166, 2,141], no standard error, and a control mean of $33,043; derived SE = 503.83 (z = 1.96).\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_wage_ate_se_derived\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"Derived SE assumes a symmetric Wald interval.\"},\n {\"id\":\"C6\",\"text\":\"RESEA Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Text states the weeks CI as unsigned magnitudes; signed table values used.\"},\n {\"id\":\"C7\",\"text\":\"The sources contain internal inconsistencies (Table 6 vs A19 SE; 608 vs 640 controls; abstract 35% vs table-implied ~30% for fatal outcomes) recorded in data_issues.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Recorded only; they do not change any extracted key.\"}\n]\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read in full\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 3 inputs match README/bundle\",\"wc -l recorded (10/134/58)\",\"exclusion ledger: 25->25, 0 removed\",\"7 data issues documented\"],\"notes\":\"No availability-rule violations\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline = direct reading of printed cells; matched source lines echoed\"],\"notes\":\"Task names all keys; no baseline_* keys needed\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py parses data/ only and writes results.json\",\"no randomness; random_seed null\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun x2: identical SHA-256\",\"stars consistent with z from printed SE for all 5 cells\",\"derived CI contains estimate\",\"wage CI symmetric; derived SE reproduces p=0.022\",\"locators complete; n_input_rows equal wc -l\"],\"notes\":\"see state/checks.md\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections citing keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('results.json'));c=json.load(open('claims.json'))\nprint([k for cl in c for k in cl['results_keys'] if k not in r]); json.load(open('state/phases.json')); print('ok')\"; ls -R","description":"Write report, claims, phase ledger; validate keys"}}]}}55{"type":"api-request","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","id":"f3d0ffb1-980e-457c-abe8-b6603f1f8392","timestamp":"2026-10-30T05:27:54.496Z","version":"2.1.314","querySource":"sdk","shapeHash":"ed2217a4794bcef594a0d666c1d3abc7d8f1f74c3de730245e10e9ba0c4acaf0","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":"fd7e2a47-0f03-43a4-a3c6-19c8efaa22e0","keep":14,"tail":["bb1774cbda0119fac172f5007569274c326b5b3b968a45bbfc04efa274599947","8afc92e64a1b5479ae5d9365d273036d71775f91b166006c43ff335a4098b3bd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7b86a48c224303699921a1dc37bb56d919821023dd77d362bf2cc581a47d04c2"}56{"parentUuid":"c7cd67bd-34d9-408e-8348-c76ac13e63fc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsLzjDW3NydZkYBP47t","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4191,"cache_read_input_tokens":34630,"output_tokens":1021,"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":4191},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6734,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsLzT6Sg1582Exg6Hgh","requestRef":"f3d0ffb1-980e-457c-abe8-b6603f1f8392","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8b657421-c0d5-4d68-82f8-ea5a0c33d119","timestamp":"2026-10-30T05:28:03.704Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0287/out","sessionId":"2f53920a-e097-4765-8002-887ca031e8bc","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"8b657421-c0d5-4d68-82f8-ea5a0c33d119","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsLzjDW3NydZkYBP47t","type":"message","role":"assistant","content":[{"type":"text","text":"I've extracted all 25 requested values. Twenty-four come exactly as printed or are derived from printed values, and one is correctly `null` because the source doesn't report it. A clean re-run of `out/analysis.py` produced an identical `out/results.json`, and all consistency checks passed.\n\n**Drug consumption room study (log points, standard errors in parentheses)**\n- **Main effect, 5 km (Table 6):** −0.431** (0.210), from 17,250 observations.\n- **Main effect, 3 km (Table 6):** −0.283 (0.279).\n- **Table 7 cells:** narcotics at 3 km is 0.549*** (0.187), fatal at 1 km is −1.207*** (0.137), and HIV at 5 km is −0.340 (0.294).\n- **Derived 95% interval for the 5 km effect:** [−0.8426, −0.0194], using estimate ± 1.96 × SE.\n- **HIV 5 km p-value:** not printed anywhere in the extract, so it is `null`. The cell has no star, so it is recorded as not significant at 10%.\n\n**Rhode Island RESEA trial (Table 2)**\n- **Wage effect:** $1,153 per year, 95% CI [166, 2,141]; control-group mean $33,043.\n- **Wage standard error:** none is printed. Assuming a symmetric interval with 1.96, the derived value is 503.83. Turned back into a p-value it gives about 0.022, the same as the printed p.\n- **Reemployment:** 1.47 percentage points.\n- **Weeks on UI:** −1.99, 95% CI [−2.24, −1.75].\n\nJSON drops trailing zeros, so 0.210 is stored as 0.21 and −0.340 as −0.34. The numbers are the same, and the report gives the printed forms.\n\nSeven problems in the sources are listed under `data_issues` in `results.json`. None changes an extracted value:\n- The same 5 km estimate has standard error 0.210 in Table 6 but 0.218 in Appendix Table A19. I used Table 6, as the task requires.\n- The A19 note says 640 control regions; the tables say 608.\n- The abstract says fatal hospitalisations fall by 35%, but the 5 km value of −0.354 log points is about −30%, which is what the paper's own Section 6.1 says.\n- The text calls the HIV estimates \"close to zero\", but the 5 km value is −0.340.\n- The RESEA text writes the weeks-on-UI interval without minus signs; Table 2 is signed, and I used the table.\n- The RESEA text has a rendering error (\"p ¡ 0.001\").\n- The RESEA tables were re-laid out by the extract editor. 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