spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:22:32.225Z","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:22:32.234Z","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}4{"parentUuid":null,"isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":"Begin"},"uuid":"45a59eda-f408-4b13-a9a2-e3936d0539a2","timestamp":"2026-10-30T05:22:32.240Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","timestamp":"2026-10-30T05:22:32.256Z","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/RC0281\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}12{"parentUuid":"0f538a2a-83f8-4053-9d26-a2ede22f30cc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrwHwPu3bw9ZPDY5Nm9","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSpAUKEAgTGAI4AUIIdGhpbmtpbmcSDNV8D4CdVRlo8tQ5TxoM83OSMYdVdAPfYyXWIjD7l31dvZ6sejdswfy8nc1pUdUP5crTkHaSvlEswJZXyLmihju+28p0Yo3H908C17UqwQRUv7ZlBQBtmQo6Ea4VxioqFJ7kpiKzptCDDV8EoZ5aoWyQhiwOA/PY5LHTDdHDBmI5+lw8yYhvmmFIMLvCSBCNdUE536FuiAcTTcq3u5dNUIkEX8EZOBhD05TbwQH21WIU96CACGfe6BPpNN3PkUgFvbmXYuqF/fCSKkzpESbqZQCkXQNk3KdvyBoK81jvmbzHFIUPJufGbxxjk5pJ9XHbTiZwVDo7Mmx4cdVazhYNNzm5PR/aZXI9AlPG4Tw0BCqrrOzvQc3A4JB3EkNvv8uXaBSZKRujV45/gyF63aVfoYqK0IxJLbFUFsKCAgIrB5j1FPy1vp2YEi/+kq5UZnM2otalsFRIgIxZYjC5gGHTE+z+FIYQi61OaqjfXF9tcfqyCQtDR2bifAhQztU3uGQ85fXsXVxfpK3rWvkds6CNK92A91Virwquab884d8eCN/OtxhRgb6IuuWnGl0RyxR9pPLWH2rtgoDDg/VlxrCDzvqOmvYVId2E0CJd05jMYRbUsTL/Bw/u0Y/P3juPj3lJwoyV+eXmRNCz0foehZr9aw6m7sJoNGvYu7/RvAIyJCuFES3LLmHaSsP4/GXIpmt2eBnBQ54uRnkOkESRQIX492LGSvI5UYOQ5n9rGpoAb30341poU+dgm66i1Q24nzgzQU1O7F7wNlxgnotGnpFCMtbQ2bLnYb8C1Z2SHUBuzkP5fKEk17LW0JpDUqpdUKtX/Q4zlTGY7fvdakWaBGtQX3OLNpT8eoFAmEQn6gQuGtKJGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":137,"output_tokens_details":{"thinking_tokens":22},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":434,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXrwHfmqSbGTedDnrzii","requestRef":"0ff95341-20dc-41ce-a9ec-5dd2ce731dc8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c8ae0479-b20b-459e-ac51-ee26e02f3984","timestamp":"2026-10-30T05:22:34.468Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"c8ae0479-b20b-459e-ac51-ee26e02f3984","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrwHwPu3bw9ZPDY5Nm9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Gnpz9YbZ6XKTGA5gkYWab9","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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nREADME.md\nsources\n\n./data/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":true,"tool_use_id":"toolu_01Gnpz9YbZ6XKTGA5gkYWab9"}]},"uuid":"5f0dd893-7b73-4c84-bdbf-3a6a7785adf2","timestamp":"2026-10-30T05:22:35.227Z","toolUseResult":"Error: Exit code 1\n# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nREADME.md\nsources\n\n./data/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","sandboxed":false,"sourceToolAssistantUUID":"bdba310c-b857-445f-a71a-3dde5b53996b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"9a0dad58d404d507fa7d2d989fa7b54b3b10dc3fc738b6728c0a42eb354a406c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Gnpz9YbZ6XKTGA5gkYWab9","name":"Bash","input":{"command":"cat TASK.md; 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wc -l data/README.md data/sources/*; shasum -a 256 data/README.md data/sources/*; cat gate_spec.json | head -50","description":"Read README, counts, hashes"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3465,"cache_read_input_tokens":4315,"output_tokens":324,"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":3465},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrwSd2p2TJLfVX9LRaq","requestRef":"b2a3b442-8625-4141-871a-6dd29b003b30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"94a0574d-ad88-42d1-900b-17e318289b27","timestamp":"2026-10-30T05:22:38.143Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"94a0574d-ad88-42d1-900b-17e318289b27","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JKHJFncYAR4UCHH95xKKhV","type":"tool_result","content":"# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. 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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. 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Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"a3aa7288-0628-451f-b504-12ef20050aab","timestamp":"2026-10-30T05:22:38.784Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2609.27732v1\n     2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n     3\tAuthors: Ana Armendariz (University of St. Gallen)\n     4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n     5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n     8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\t================================================================\n    11\t\n    12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n    13\t\n    14\tAna Armendariz*\n    15\t\n    16\tSeptember 23, 2026\n    17\t\n    18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n    19\t\n    20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n    21\t\n    22\t## 6 Results\n    23\t\n    24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n    25\t\n    26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n    27\t\n    28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n    29\t\n    30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n    31\t\n    32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n    33\t\n    34\t| 1 km | 3 km | 5 km\n    35\t\n    36\tATT | -0.399 | -0.283 | -0.431**\n    37\t\n    38\t| (0.412) | (0.279) | (0.210)\n    39\t\n    40\t% change | -32.9% | -24.7% | -35.0%\n    41\t\n    42\tTreated regions | 7 | 35 | 50\n    43\t\n    44\tControl regions | 608 | 608 | 608\n    45\t\n    46\tObservations | 16,175 | 16,875 | 17,250\n    47\t\n    48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n    49\tthroughout. Regions near DCRs that opened before 1998\n    50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n    51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n    52\t{}^{*}p<0.10 .\n    53\t\n    54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n    55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n    56\t\n    57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n    58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n    59\t\n    60\tFigure 7: Event study: Sub-outcome estimates,\n    61\twithin 5 km, \\pm 5-year window\n    62\t\n    63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n    64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n    65\t\n    66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n    67\t\n    68\tOutcome | 1 km | 3 km | 5 km\n    69\t\n    70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n    71\t\n    72\t| (0.382) | (0.244) | (0.220)\n    73\t\n    74\tHepatitis | -0.270 | -0.413 | -0.406\n    75\t\n    76\t| (0.486) | (0.270) | (0.250)\n    77\t\n    78\tHIV | 0.063 | -0.239 | -0.340\n    79\t\n    80\t| (0.801) | (0.337) | (0.294)\n    81\t\n    82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n    83\t\n    84\t| (0.288) | (0.187) | (0.165)\n    85\t\n    86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n    87\t\n    88\t| (0.137) | (0.215) | (0.178)\n    89\t\n    90\tTreated regions | 7 | 35 | 50\n    91\t\n    92\tControl regions | 608 (never-treated) | |\n    93\t\n    94\tNote: Each cell reports the overall ATT from\n    95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n    96\tnever-treated controls and a universal base period. Outcomes are\n    97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n    98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n    99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n   100\testimates are identified from a single treated region per cohort and\n   101\tshould be interpreted with caution. Regions near DCRs opened before\n   102\t1998 excluded. Standard errors clustered at the Medstat region level.\n   103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n   104\t\n   105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n   106\t\n   107\t### 6.1 Mechanisms\n   108\t\n   109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n   110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n   111\t(Kerr et al., 2017; Hedrich, 2004).\n   112\t\n   113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n   114\t\n   115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n   116\t\n   117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n   118\t\n   119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n   120\t\n   121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n   122\t\n   123\t| (0.218) | (0.208) | (0.205) | (0.038)\n   124\t\n   125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n   126\t\n   127\tTreated regions | 50 | | |\n   128\t\n   129\tControl regions | 608 | | |\n   130\t\n   131\tObservations | 17,250 | | |\n   132\t\n   133\tNote: All specifications use the 5 km buffer,\n   134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"801ca76d-dbfc-48d7-bbdd-ba7520de1c3e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"a3aa7288-0628-451f-b504-12ef20050aab","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrwSuA9vG2WEGgNa2dR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JBKExC1zRVPnVwRDqKzHMX","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt","description":"Show RESEA source extract"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3465,"cache_read_input_tokens":4315,"output_tokens":324,"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":3465},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXrwSd2p2TJLfVX9LRaq","requestRef":"b2a3b442-8625-4141-871a-6dd29b003b30","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9dd450e1-6e72-42a5-bfd6-a85840f4df4a","timestamp":"2026-10-30T05:22:39.312Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"9dd450e1-6e72-42a5-bfd6-a85840f4df4a","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JBKExC1zRVPnVwRDqKzHMX","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model","is_error":false}]},"uuid":"3c2c125d-32b5-4040-be2b-25b678ccf2e9","timestamp":"2026-10-30T05:22:39.356Z","toolUseResult":{"stdout":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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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{\n  \"task_id\": \"F4-conf-01\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\"\n  ],\n  \"results_keys\": {\n    \"dcr_att_5km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_5km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_obs_5km\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_narcotics_att_3km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_fatal_att_1km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_fatal_att_1km_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_hiv_att_5km\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"dcr_hiv_att_5km_se\": {","is_error":false},{"tool_use_id":"toolu_01Wm74g4wVRDpEoXausrzanG","type":"tool_result","content":"     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},{"tool_use_id":"toolu_01JBKExC1zRVPnVwRDqKzHMX","type":"tool_result","content":"     1\tSOURCE EXTRACT — arxiv-2606.14621v1\n     2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n     3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n     4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n     5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n     6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n     7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n     8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n     9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n    10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n    11\t================================================================\n    12\t\n    13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n    14\t\n    15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n    16\t\n    17\t###### Abstract\n    18\t\n    19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n    20\t\n    21\t## 3 Results\n    22\t\n    23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n    24\t\n    25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n    26\t\n    27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n    28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n    29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n    30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n    31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n    32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n    33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n    34\t\n    35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n    36\t\n    37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n    38\t\n    39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n    40\t\n    41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n    42\t\n    43\t### 3.1 Cost-benefit analysis\n    44\t\n    45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n    46\t\n    47\t### 4.1 Methodological lessons (excerpt)\n    48\t\n    49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n    50\t\n    51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n    52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n    53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n    54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n    55\t\n    56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n    57\t\n    58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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> out/state/contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Extract specified treatment effects and their uncertainty, exactly as printed, from two extracts: Armendariz (2026) arXiv:2609.27732v1 (Swiss DCR staggered DiD; Tables 6, 7; log points; SEs in parentheses; stars) and Li, Pearson-Merkowitz & Yokum (2026) arXiv:2606.14621v1 (RI RESEA RCT; Table 2; ATEs with p-values and 95% CIs). Derive (a) a 95% CI for the Table 6 5 km ATT from its SE and (b) an SE for the Table 2 wage ATE from its 95% CI, stating the convention; recognise unreported quantities (exact p-value of HIV 5 km ATT; SE of wage ATE).\n**Estimands.** The printed numbers themselves (no re-estimation). Derived values assume approximate normality of the estimator (Wald-type, symmetric CI) with critical value 1.96.\n**Outputs.** out/results.json (keys listed in TASK.md: dcr_att_5km(_se), dcr_att_3km(_se), dcr_obs_5km, dcr_narcotics_att_3km(_se), dcr_fatal_att_1km(_se), dcr_hiv_att_5km(_se), dcr_hiv_att_5km_p_value, dcr_hiv_att_5km_significant_10pct, dcr_att_5km_ci_low/high, ri_wage_ate, ri_wage_ate_ci_low/high, ri_wage_ate_se_reported, ri_wage_ate_se_derived, ri_control_mean_wage, ri_reemployment_ate_pp, ri_weeks_ui_ate, ri_weeks_ui_ate_ci_low/high, 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 are admissible; values as printed (sign, decimals); when a value appears in several tables use the named table (e.g. Table 6 not A19; Table 2 not Table 3); locators in prescribed format; unreported -> null with \"not reported in <source>\".\n**Invalidity.** Using A19 / Table 3 / prose values instead of the named table; rounding; inventing a p-value; missing locator.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2083,"cache_read_input_tokens":19918,"output_tokens":1270,"output_tokens_details":{"thinking_tokens":121},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2083},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrxjFSJnP4aHHr3hD2K","requestRef":"01164481-1416-4a90-9664-29419a235c1d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e25d57f2-80b4-4192-a6a7-7bc6fcad1ef7","timestamp":"2026-10-30T05:23:04.140Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"e25d57f2-80b4-4192-a6a7-7bc6fcad1ef7","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Qrx2kQMQbJw938PbsiCV13","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"6456c669-d486-4f04-9334-b78bec8e45a1","timestamp":"2026-10-30T05:23:04.194Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e25d57f2-80b4-4192-a6a7-7bc6fcad1ef7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"261d1c318612c40835724afa754cbc4b0ec2488e0b9e310eb67262092251849b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Qrx2kQMQbJw938PbsiCV13","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F4-conf-01\n**Question.** Extract specified treatment effects and their uncertainty, exactly as printed, from two extracts: Armendariz (2026) arXiv:2609.27732v1 (Swiss DCR staggered DiD; Tables 6, 7; log points; SEs in parentheses; stars) and Li, Pearson-Merkowitz & Yokum (2026) arXiv:2606.14621v1 (RI RESEA RCT; Table 2; ATEs with p-values and 95% CIs). Derive (a) a 95% CI for the Table 6 5 km ATT from its SE and (b) an SE for the Table 2 wage ATE from its 95% CI, stating the convention; recognise unreported quantities (exact p-value of HIV 5 km ATT; SE of wage ATE).\n**Estimands.** The printed numbers themselves (no re-estimation). Derived values assume approximate normality of the estimator (Wald-type, symmetric CI) with critical value 1.96.\n**Outputs.** out/results.json (keys listed in TASK.md: dcr_att_5km(_se), dcr_att_3km(_se), dcr_obs_5km, dcr_narcotics_att_3km(_se), dcr_fatal_att_1km(_se), dcr_hiv_att_5km(_se), dcr_hiv_att_5km_p_value, dcr_hiv_att_5km_significant_10pct, dcr_att_5km_ci_low/high, ri_wage_ate, ri_wage_ate_ci_low/high, ri_wage_ate_se_reported, ri_wage_ate_se_derived, ri_control_mean_wage, ri_reemployment_ate_pp, ri_weeks_ui_ate, ri_weeks_ui_ate_ci_low/high, 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 are admissible; values as printed (sign, decimals); when a value appears in several tables use the named table (e.g. Table 6 not A19; Table 2 not Table 3); locators in prescribed format; unreported -> null with \"not reported in <source>\".\n**Invalidity.** Using A19 / Table 3 / prose values instead of the named table; rounding; inventing a p-value; missing locator.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | n/a |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain-text extracts; tables one row per line, cells separated by \" | \"; DCR SE rows begin with \"| (\" on the line after the coefficient row (blank line between). RESEA Tables 2/3 re-set one row per outcome; each cell \"estimate; p=...; 95% CI: [lo, hi]\".\nUnits: DCR ATTs in log points, Observations in region-years (thousands separators); RESEA wages USD/yr with \"$\" and thousands commas, reemployment pp, weeks.\nMissing: no exact p-values in DCR tables (stars only); no SEs in RESEA Table 2 (CIs only). No duplicated rows.\n\nAnomalies / internal inconsistencies noted (none affects the requested cells; handled by using the table named in each key):\n1. The Table 6 5 km SE is (0.210) but Appendix Table A19 col CS-DR prints the same ATT -0.431** with SE (0.218). Key names Table 6 -> 0.210 used.\n2. Table 6/7 and Figure 6 say 608 never-treated control regions; A19 note says 640. Table 6 observation counts (16,175 / 16,875 / 17,250) equal 25 x (640 + treated regions 7/35/50), i.e. consistent with 640, not 608, controls. Reported as printed; not resolved.\n3. Abstract says fatal outcomes \"fall by 35%\"; Table 7 fatal 5 km ATT -0.354 corresponds to exp(-0.354)-1 = -29.8% (Sec. 6.1 says ~30%). Log points vs percent conflation in abstract; not used.\n4. Prose (Sec. 6) calls HIV estimates \"close to zero\" although 3 km/5 km ATTs are -0.239/-0.340 (insignificant). Not used.\n5. RESEA prose gives the weeks-on-UI CI as magnitudes [1.75, 2.24]; Table 2 prints [-2.24, -1.75]. Table 2 used.\n6. RESEA prose uses \"p ¡ 0.001\" (rendering defect for \"<\"). Not used.\nAvailability: no input violates the rules; Table 3 of RESEA duplicates Table 2 ATE column, A19 duplicates Table 6 5 km - named tables used.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (25 value/boolean keys) assessed; none excluded\",25,25,0,\"Pure extraction task; every requested item is assessed; the unreported HIV p-value is kept as null rather than excluded\"\nEOF\necho \"- 2026-10-30 P1 passed: hashes match README; 6 anomalies logged in data_audit.md.\" >> out/state/log.md","description":"Write data audit and exclusion 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | matches README hash |\n|---|---|---|---|\n| data/README.md | 16ebdff389eeaee9508fe81c4edc43690f31f3463c469680c814134086ef9929 | 10 | n/a |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | yes |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | yes |\n\nSchema: plain-text extracts; tables one row per line, cells separated by \" | \"; DCR SE rows begin with \"| (\" on the line after the coefficient row (blank line between). RESEA Tables 2/3 re-set one row per outcome; each cell \"estimate; p=...; 95% CI: [lo, hi]\".\nUnits: DCR ATTs in log points, Observations in region-years (thousands separators); RESEA wages USD/yr with \"$\" and thousands commas, reemployment pp, weeks.\nMissing: no exact p-values in DCR tables (stars only); no SEs in RESEA Table 2 (CIs only). No duplicated rows.\n\nAnomalies / internal inconsistencies noted (none affects the requested cells; handled by using the table named in each key):\n1. The Table 6 5 km SE is (0.210) but Appendix Table A19 col CS-DR prints the same ATT -0.431** with SE (0.218). Key names Table 6 -> 0.210 used.\n2. Table 6/7 and Figure 6 say 608 never-treated control regions; A19 note says 640. Table 6 observation counts (16,175 / 16,875 / 17,250) equal 25 x (640 + treated regions 7/35/50), i.e. consistent with 640, not 608, controls. Reported as printed; not resolved.\n3. Abstract says fatal outcomes \"fall by 35%\"; Table 7 fatal 5 km ATT -0.354 corresponds to exp(-0.354)-1 = -29.8% (Sec. 6.1 says ~30%). Log points vs percent conflation in abstract; not used.\n4. Prose (Sec. 6) calls HIV estimates \"close to zero\" although 3 km/5 km ATTs are -0.239/-0.340 (insignificant). Not used.\n5. RESEA prose gives the weeks-on-UI CI as magnitudes [1.75, 2.24]; Table 2 prints [-2.24, -1.75]. Table 2 used.\n6. RESEA prose uses \"p ¡ 0.001\" (rendering defect for \"<\"). Not used.\nAvailability: no input violates the rules; Table 3 of RESEA duplicates Table 2 ATE column, A19 duplicates Table 6 5 km - named tables used.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,\"Requested items (25 value/boolean keys) assessed; none excluded\",25,25,0,\"Pure extraction task; every requested item is assessed; the unreported HIV p-value is kept as null rather than excluded\"\nEOF\necho \"- 2026-10-30 P1 passed: hashes match README; 6 anomalies logged in data_audit.md.\" >> out/state/log.md","description":"Write data audit and exclusion ledger"}}]}}44{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"ef067029-e1e6-49a9-9f70-ef3d9de8ae77","timestamp":"2026-10-30T05:23:22.198Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"ae89cfb9-a139-481a-ada4-a53e4cf36c67","keep":10,"tail":["af1b3a3eaa3924bc55169558addc48b831d849d0e7cc110f8d82c1dd88e2455d","582753f91d54d177dccdc33386243ff0b3844ab989828bf5e449b804882e7f38"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a223ccfcb817d713bed9e58d1179c0ef111a8ee55c6d8c36b61ce5437e478550"}45{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"628b46b1-d44a-454d-87ba-014b874bd2ab","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}46{"type":"atis-latch","atis":"","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}47{"parentUuid":"628b46b1-d44a-454d-87ba-014b874bd2ab","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrzujGnQZfcoLG19t5t","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (parsing the tables, printing matched lines)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1745,"cache_read_input_tokens":23286,"output_tokens":5300,"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":1745},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXrzuSudjBHuLqd1U7D8","requestRef":"ef067029-e1e6-49a9-9f70-ef3d9de8ae77","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8965ca6d-68bb-4423-8165-f1ceb42dc44a","timestamp":"2026-10-30T05:23:23.924Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"8965ca6d-68bb-4423-8165-f1ceb42dc44a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXrzujGnQZfcoLG19t5t","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LFGCq9FodhUGzgJkKnLARQ","name":"Bash","input":{"command":"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. No randomness.\"\"\"\nimport json, re, os, hashlib\nfrom decimal import Decimal\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for 95% two-sided\n\ndef read(p):\n    with open(os.path.join(D, p), encoding=\"utf-8\") as f:\n        return f.read()\n\ndef wc_l(p):\n    with open(os.path.join(D, p), \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\ndcr = read(f\"sources/{DCR_ID}.txt\").split(\"\\n\")\nri = read(f\"sources/{RI_ID}.txt\").split(\"\\n\")\n\ndef table_block(lines, title_prefix):\n    i = next(k for k, l in enumerate(lines) if l.startswith(title_prefix))\n    j = next(k for k in range(i + 1, len(lines)) if lines[k].startswith(\"Note:\"))\n    return [(k + 1, lines[k]) for k in range(i, j) if lines[k].strip()]\n\ndef num(s):\n    \"\"\"Parse printed number keeping printed decimals; returns (float, printed str).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"()\")\n    s = s.replace(\"%\", \"\")\n    return (int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)), s\n\ndef dcr_cell(block, row, col):\n    header = [c.strip() for c in block[1][1].split(\"|\")]\n    ci = header.index(col)\n    for idx, (ln, l) in enumerate(block):\n        cells = [c.strip() for c in l.split(\"|\")]\n        if cells[0] == row:\n            raw = cells[ci]\n            print(f\"[{DCR_ID} line {ln}] {l}\")\n            out = {\"value\": num(raw)[0], \"stars\": raw.count(\"*\"), \"raw\": raw}\n            nxt = block[idx + 1] if idx + 1 < len(block) else None\n            if nxt and nxt[1].lstrip().startswith(\"| (\"):\n                print(f\"[{DCR_ID} line {nxt[0]}] {nxt[1]}\")\n                se_raw = [c.strip() for c in nxt[1].split(\"|\")][ci]\n                out[\"se\"] = num(se_raw)[0]; out[\"se_raw\"] = se_raw\n            return out\n    raise KeyError(row)\n\nt6 = table_block(dcr, \"Table 6:\")\nt7 = table_block(dcr, \"Table 7:\")\nfor ln, l in t6[:2] + t7[:2]:\n    print(f\"[{DCR_ID} line {ln}] {l}\")\n\na5 = dcr_cell(t6, \"ATT\", \"5 km\"); a3 = dcr_cell(t6, \"ATT\", \"3 km\")\nobs5 = dcr_cell(t6, \"Observations\", \"5 km\")\nnarc3 = dcr_cell(t7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(t7, \"HIV\", \"5 km\")\n\n# exact p-value search: any \"p=\" / \"p-value\" in DCR extract tied to HIV?\ndcr_text = \"\\n\".join(dcr)\np_hits = [l for l in dcr if re.search(r\"p\\s*=\\s*0?\\.\\d\", l)]\nhiv_p = None  # stars only (none on HIV 5 km); no exact p-value printed\nprint(f\"DCR lines with an exact 'p=' value: {len(p_hits)}\")\n\n# derived CI (Table 6, 5 km); round to 4 dp (one more than the printed 3 dp)\nci_lo = round(a5[\"value\"] - Z * a5[\"se\"], 4)\nci_hi = round(a5[\"value\"] + Z * a5[\"se\"], 4)\n\n# RESEA Table 2\nt2_hdr = next(k for k, l in enumerate(ri) if l.startswith(\"Outcome | Treatment | Control\"))\nheader = [c.strip() for c in ri[t2_hdr].split(\"|\")]\nprint(f\"[{RI_ID} line {t2_hdr+1}] {ri[t2_hdr]}\")\ndef ri_row(label):\n    for k in range(t2_hdr + 1, len(ri)):\n        cells = [c.strip() for c in ri[k].split(\"|\")]\n        if cells[0] == label:\n            print(f\"[{RI_ID} line {k+1}] {ri[k]}\")\n            return dict(zip(header, cells))\n    raise KeyError(label)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(cell):\n    est = cell.split(\";\")[0]\n    est = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", est.strip())\n    m = re.search(r\"95% CI: \\[(.+?),\\s*(-?[\\d.]+(?:,\\d{3})*(?:\\.\\d+)?)\\]\", cell)\n    lo, hi = m.group(1), m.group(2)\n    se_printed = bool(re.search(r\"\\bSE\\b|standard error\", cell, re.I))\n    return num(est)[0], num(lo)[0], num(hi)[0], se_printed\nw = ri_row(\"Wage\"); r = ri_row(\"Reemployment\"); u = ri_row(\"Weeks on UI\")\nw_ate, w_lo, w_hi, w_se_printed = parse_effect(w[ATE])\nr_ate, _, _, _ = parse_effect(r[ATE])\nu_ate, u_lo, u_hi, _ = parse_effect(u[ATE])\nctrl_mean = num(w[\"Control\"].split(\"(\")[0])[0]\nw_se = round((w_hi - w_lo) / (2 * Z), 2)\n\nS6 = f\"{DCR_ID} | Table 6\"; S7 = f\"{DCR_ID} | Table 7\"; S2 = f\"{RI_ID} | Table 2\"\nloc = {\n \"dcr_att_5km\": f'{S6} | row \"ATT\" | column \"5 km\"',\n \"dcr_att_5km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_att_3km\": f'{S6} | row \"ATT\" | column \"3 km\"',\n \"dcr_att_3km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_obs_5km\": f'{S6} | row \"Observations\" | column \"5 km\"',\n \"dcr_narcotics_att_3km\": f'{S7} | row \"Narcotics\" | column \"3 km\"',\n \"dcr_narcotics_att_3km_se\": f'{S7} | row \"Narcotics\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_fatal_att_1km\": f'{S7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n \"dcr_fatal_att_1km_se\": f'{S7} | row \"Drug-related (fatal)\" (standard error line in parentheses) | column \"1 km\"',\n \"dcr_hiv_att_5km\": f'{S7} | row \"HIV\" | column \"5 km\"',\n \"dcr_hiv_att_5km_se\": f'{S7} | row \"HIV\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"dcr_att_5km_ci_high\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"ri_wage_ate\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\"',\n \"ri_wage_ate_ci_low\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_wage_ate_ci_high\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n \"ri_wage_ate_se_derived\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in {RI_ID}',\n \"ri_control_mean_wage\": f'{S2} | row \"Wage\" | column \"Control\"',\n \"ri_reemployment_ate_pp\": f'{S2} | row \"Reemployment\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate_ci_low\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_weeks_ui_ate_ci_high\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\n\nissues = [\n {\"issue\": f\"{DCR_ID}: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n  \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"},\n {\"issue\": f\"{DCR_ID}: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n  \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"},\n {\"issue\": f\"{DCR_ID}: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n  \"handling\": \"Prose not used; table values extracted as printed.\"},\n {\"issue\": f\"{RI_ID}: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n  \"handling\": \"Table 2 values used (lower bound -2.24 first).\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": a5[\"value\"], \"dcr_att_5km_se\": a5[\"se\"],\n \"dcr_att_3km\": a3[\"value\"], \"dcr_att_3km_se\": a3[\"se\"],\n \"dcr_obs_5km\": int(obs5[\"value\"]),\n \"dcr_narcotics_att_3km\": narc3[\"value\"], \"dcr_narcotics_att_3km_se\": narc3[\"se\"],\n \"dcr_fatal_att_1km\": fat1[\"value\"], \"dcr_fatal_att_1km_se\": fat1[\"se\"],\n \"dcr_hiv_att_5km\": hiv5[\"value\"], \"dcr_hiv_att_5km_se\": hiv5[\"se\"],\n \"dcr_hiv_att_5km_p_value\": hiv_p,\n \"dcr_hiv_att_5km_significant_10pct\": hiv5[\"stars\"] >= 1,\n \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n \"ri_wage_ate\": w_ate, \"ri_wage_ate_ci_low\": w_lo, \"ri_wage_ate_ci_high\": w_hi,\n \"ri_wage_ate_se_reported\": w_se_printed,\n \"ri_wage_ate_se_derived\": w_se,\n \"ri_control_mean_wage\": ctrl_mean,\n \"ri_reemployment_ate_pp\": r_ate,\n \"ri_weeks_ui_ate\": u_ate, \"ri_weeks_ui_ate_ci_low\": u_lo, \"ri_weeks_ui_ate_ci_high\": u_hi,\n \"locators\": loc,\n \"derivation_notes\": {\n  \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n  \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"README.md\": wc_l(\"README.md\"),\n                  f\"sources/{DCR_ID}.txt\": wc_l(f\"sources/{DCR_ID}.txt\"),\n                  f\"sources/{RI_ID}.txt\": wc_l(f\"sources/{RI_ID}.txt\")},\n \"n_analysed\": 25,\n \"random_seed\": None,\n}\n# consistency diagnostics (printed only)\nimport math\nzw = w_ate / w_se\nprint(f\"Check: wage ATE z={zw:.3f}, implied two-sided normal p={math.erfc(abs(zw)/math.sqrt(2)):.4f} (printed p=0.022)\")\nprint(f\"Check: DCR 5km |t|={abs(a5['value'])/a5['se']:.3f} (printed **, i.e. p<0.05)\")\nprint(f\"Check: HIV 5km |t|={abs(hiv5['value'])/hiv5['se']:.3f} (no star)\")\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"locators\", \"derivation_notes\", \"data_issues\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1745,"cache_read_input_tokens":23286,"output_tokens":5300,"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":1745},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXrzuSudjBHuLqd1U7D8","requestRef":"ef067029-e1e6-49a9-9f70-ef3d9de8ae77","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3a401c1e-dedd-40b1-a6fa-db7671cfa58c","timestamp":"2026-10-30T05:24:02.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"3a401c1e-dedd-40b1-a6fa-db7671cfa58c","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LFGCq9FodhUGzgJkKnLARQ","type":"tool_result","content":"[arxiv-2609.27732v1 line 32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1 line 34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1 line 68] Outcome | 1 km | 3 km | 5 km\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)\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)\n[arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\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)\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)\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)\nDCR lines with an exact 'p=' value: 0\n[arxiv-2606.14621v1 line 27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\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]\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]\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]\nCheck: wage ATE z=2.288, implied two-sided normal p=0.0221 (printed p=0.022)\nCheck: DCR 5km |t|=2.052 (printed **, i.e. p<0.05)\nCheck: HIV 5km |t|=1.156 (no star)\n{\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"README.md\": 10,\n  \"sources/arxiv-2609.27732v1.txt\": 134,\n  \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null\n}","is_error":false}]},"uuid":"24216d81-8329-4320-bf8e-573a73e9797d","timestamp":"2026-10-30T05:24:02.530Z","toolUseResult":{"stdout":"[arxiv-2609.27732v1 line 32] Table 6: Overall Average Treatment Effect on the Treated (ATT)\n[arxiv-2609.27732v1 line 34] | 1 km | 3 km | 5 km\n[arxiv-2609.27732v1 line 66] Table 7: Overall ATT by Outcome Category and Buffer Zone\n[arxiv-2609.27732v1 line 68] Outcome | 1 km | 3 km | 5 km\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)\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)\n[arxiv-2609.27732v1 line 46] Observations | 16,175 | 16,875 | 17,250\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)\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)\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)\nDCR lines with an exact 'p=' value: 0\n[arxiv-2606.14621v1 line 27] Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\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]\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]\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]\nCheck: wage ATE z=2.288, implied two-sided normal p=0.0221 (printed p=0.022)\nCheck: DCR 5km |t|=2.052 (printed **, i.e. p<0.05)\nCheck: HIV 5km |t|=1.156 (no star)\n{\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.21,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_obs_5km\": 17250,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.34,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_p_value\": null,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_att_5km_ci_low\": -0.8426,\n \"dcr_att_5km_ci_high\": -0.0194,\n \"ri_wage_ate\": 1153,\n \"ri_wage_ate_ci_low\": 166,\n \"ri_wage_ate_ci_high\": 2141,\n \"ri_wage_ate_se_reported\": false,\n \"ri_wage_ate_se_derived\": 503.83,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"README.md\": 10,\n  \"sources/arxiv-2609.27732v1.txt\": 134,\n  \"sources/arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3a401c1e-dedd-40b1-a6fa-db7671cfa58c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"7c43cbf21e852edc8a7045eb6193cf31b4822c052d21285a8b1dcdbfc4585771","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (parsing the tables, printing matched lines)."},{"type":"tool_use","id":"toolu_01LFGCq9FodhUGzgJkKnLARQ","name":"Bash","input":{"command":"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. No randomness.\"\"\"\nimport json, re, os, hashlib\nfrom decimal import Decimal\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nZ = 1.96  # normal critical value for 95% two-sided\n\ndef read(p):\n    with open(os.path.join(D, p), encoding=\"utf-8\") as f:\n        return f.read()\n\ndef wc_l(p):\n    with open(os.path.join(D, p), \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\ndcr = read(f\"sources/{DCR_ID}.txt\").split(\"\\n\")\nri = read(f\"sources/{RI_ID}.txt\").split(\"\\n\")\n\ndef table_block(lines, title_prefix):\n    i = next(k for k, l in enumerate(lines) if l.startswith(title_prefix))\n    j = next(k for k in range(i + 1, len(lines)) if lines[k].startswith(\"Note:\"))\n    return [(k + 1, lines[k]) for k in range(i, j) if lines[k].strip()]\n\ndef num(s):\n    \"\"\"Parse printed number keeping printed decimals; returns (float, printed str).\"\"\"\n    s = s.strip().replace(\"$\", \"\").replace(\",\", \"\").replace(\"*\", \"\").strip(\"()\")\n    s = s.replace(\"%\", \"\")\n    return (int(s) if re.fullmatch(r\"-?\\d+\", s) else float(s)), s\n\ndef dcr_cell(block, row, col):\n    header = [c.strip() for c in block[1][1].split(\"|\")]\n    ci = header.index(col)\n    for idx, (ln, l) in enumerate(block):\n        cells = [c.strip() for c in l.split(\"|\")]\n        if cells[0] == row:\n            raw = cells[ci]\n            print(f\"[{DCR_ID} line {ln}] {l}\")\n            out = {\"value\": num(raw)[0], \"stars\": raw.count(\"*\"), \"raw\": raw}\n            nxt = block[idx + 1] if idx + 1 < len(block) else None\n            if nxt and nxt[1].lstrip().startswith(\"| (\"):\n                print(f\"[{DCR_ID} line {nxt[0]}] {nxt[1]}\")\n                se_raw = [c.strip() for c in nxt[1].split(\"|\")][ci]\n                out[\"se\"] = num(se_raw)[0]; out[\"se_raw\"] = se_raw\n            return out\n    raise KeyError(row)\n\nt6 = table_block(dcr, \"Table 6:\")\nt7 = table_block(dcr, \"Table 7:\")\nfor ln, l in t6[:2] + t7[:2]:\n    print(f\"[{DCR_ID} line {ln}] {l}\")\n\na5 = dcr_cell(t6, \"ATT\", \"5 km\"); a3 = dcr_cell(t6, \"ATT\", \"3 km\")\nobs5 = dcr_cell(t6, \"Observations\", \"5 km\")\nnarc3 = dcr_cell(t7, \"Narcotics\", \"3 km\")\nfat1 = dcr_cell(t7, \"Drug-related (fatal)\", \"1 km\")\nhiv5 = dcr_cell(t7, \"HIV\", \"5 km\")\n\n# exact p-value search: any \"p=\" / \"p-value\" in DCR extract tied to HIV?\ndcr_text = \"\\n\".join(dcr)\np_hits = [l for l in dcr if re.search(r\"p\\s*=\\s*0?\\.\\d\", l)]\nhiv_p = None  # stars only (none on HIV 5 km); no exact p-value printed\nprint(f\"DCR lines with an exact 'p=' value: {len(p_hits)}\")\n\n# derived CI (Table 6, 5 km); round to 4 dp (one more than the printed 3 dp)\nci_lo = round(a5[\"value\"] - Z * a5[\"se\"], 4)\nci_hi = round(a5[\"value\"] + Z * a5[\"se\"], 4)\n\n# RESEA Table 2\nt2_hdr = next(k for k, l in enumerate(ri) if l.startswith(\"Outcome | Treatment | Control\"))\nheader = [c.strip() for c in ri[t2_hdr].split(\"|\")]\nprint(f\"[{RI_ID} line {t2_hdr+1}] {ri[t2_hdr]}\")\ndef ri_row(label):\n    for k in range(t2_hdr + 1, len(ri)):\n        cells = [c.strip() for c in ri[k].split(\"|\")]\n        if cells[0] == label:\n            print(f\"[{RI_ID} line {k+1}] {ri[k]}\")\n            return dict(zip(header, cells))\n    raise KeyError(label)\nATE = \"Average treatment effect estimate\"\ndef parse_effect(cell):\n    est = cell.split(\";\")[0]\n    est = re.sub(r\"\\s*(pp|weeks|wks)$\", \"\", est.strip())\n    m = re.search(r\"95% CI: \\[(.+?),\\s*(-?[\\d.]+(?:,\\d{3})*(?:\\.\\d+)?)\\]\", cell)\n    lo, hi = m.group(1), m.group(2)\n    se_printed = bool(re.search(r\"\\bSE\\b|standard error\", cell, re.I))\n    return num(est)[0], num(lo)[0], num(hi)[0], se_printed\nw = ri_row(\"Wage\"); r = ri_row(\"Reemployment\"); u = ri_row(\"Weeks on UI\")\nw_ate, w_lo, w_hi, w_se_printed = parse_effect(w[ATE])\nr_ate, _, _, _ = parse_effect(r[ATE])\nu_ate, u_lo, u_hi, _ = parse_effect(u[ATE])\nctrl_mean = num(w[\"Control\"].split(\"(\")[0])[0]\nw_se = round((w_hi - w_lo) / (2 * Z), 2)\n\nS6 = f\"{DCR_ID} | Table 6\"; S7 = f\"{DCR_ID} | Table 7\"; S2 = f\"{RI_ID} | Table 2\"\nloc = {\n \"dcr_att_5km\": f'{S6} | row \"ATT\" | column \"5 km\"',\n \"dcr_att_5km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_att_3km\": f'{S6} | row \"ATT\" | column \"3 km\"',\n \"dcr_att_3km_se\": f'{S6} | row \"ATT\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_obs_5km\": f'{S6} | row \"Observations\" | column \"5 km\"',\n \"dcr_narcotics_att_3km\": f'{S7} | row \"Narcotics\" | column \"3 km\"',\n \"dcr_narcotics_att_3km_se\": f'{S7} | row \"Narcotics\" (standard error line in parentheses) | column \"3 km\"',\n \"dcr_fatal_att_1km\": f'{S7} | row \"Drug-related (fatal)\" | column \"1 km\"',\n \"dcr_fatal_att_1km_se\": f'{S7} | row \"Drug-related (fatal)\" (standard error line in parentheses) | column \"1 km\"',\n \"dcr_hiv_att_5km\": f'{S7} | row \"HIV\" | column \"5 km\"',\n \"dcr_hiv_att_5km_se\": f'{S7} | row \"HIV\" (standard error line in parentheses) | column \"5 km\"',\n \"dcr_hiv_att_5km_p_value\": f\"not reported in {DCR_ID} (Table 7 gives only significance stars; the HIV 5 km cell has none)\",\n \"dcr_att_5km_ci_low\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"dcr_att_5km_ci_high\": f'{S6} | row \"ATT\" | column \"5 km\" | derived from printed ATT and SE (normal, 1.96)',\n \"ri_wage_ate\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\"',\n \"ri_wage_ate_ci_low\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_wage_ate_ci_high\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n \"ri_wage_ate_se_derived\": f'{S2} | row \"Wage\" | column \"Average treatment effect estimate\" | derived from printed 95% CI (width / (2 x 1.96)); SE not reported in {RI_ID}',\n \"ri_control_mean_wage\": f'{S2} | row \"Wage\" | column \"Control\"',\n \"ri_reemployment_ate_pp\": f'{S2} | row \"Reemployment\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\"',\n \"ri_weeks_ui_ate_ci_low\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI lower bound)',\n \"ri_weeks_ui_ate_ci_high\": f'{S2} | row \"Weeks on UI\" | column \"Average treatment effect estimate\" (95% CI upper bound)',\n}\n\nissues = [\n {\"issue\": f\"{DCR_ID}: Table 6 prints SE (0.210) for the 5 km ATT -0.431**, while Appendix Table A19 (CS-DR column) prints the same ATT with SE (0.218).\",\n  \"handling\": \"Used Table 6 value (0.210) as the key definition names Table 6; discrepancy reported.\"},\n {\"issue\": f\"{DCR_ID}: Tables 6/7 and Figure 6 state 608 never-treated control regions, the A19 note states 640; the Table 6 observation counts (16,175/16,875/17,250) equal 25 x (640 + 7/35/50 treated regions), i.e. consistent with 640 controls.\",\n  \"handling\": \"Observation count reported as printed (17250); inconsistency not resolvable from the extract, reported only.\"},\n {\"issue\": f\"{DCR_ID}: abstract says fatal hospitalizations 'fall by 35%', whereas Table 7 fatal 5 km ATT -0.354 log points implies about -29.8% (Section 6.1 says ~30%); prose also calls HIV estimates 'close to zero' though 3/5 km ATTs are -0.239/-0.340.\",\n  \"handling\": \"Prose not used; table values extracted as printed.\"},\n {\"issue\": f\"{RI_ID}: prose reports the weeks-on-UI CI as magnitudes [1.75 weeks, 2.24 weeks]; Table 2 prints [-2.24, -1.75]. Prose also has a rendering defect 'p ¡ 0.001'.\",\n  \"handling\": \"Table 2 values used (lower bound -2.24 first).\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-01\",\n \"dcr_att_5km\": a5[\"value\"], \"dcr_att_5km_se\": a5[\"se\"],\n \"dcr_att_3km\": a3[\"value\"], \"dcr_att_3km_se\": a3[\"se\"],\n \"dcr_obs_5km\": int(obs5[\"value\"]),\n \"dcr_narcotics_att_3km\": narc3[\"value\"], \"dcr_narcotics_att_3km_se\": narc3[\"se\"],\n \"dcr_fatal_att_1km\": fat1[\"value\"], \"dcr_fatal_att_1km_se\": fat1[\"se\"],\n \"dcr_hiv_att_5km\": hiv5[\"value\"], \"dcr_hiv_att_5km_se\": hiv5[\"se\"],\n \"dcr_hiv_att_5km_p_value\": hiv_p,\n \"dcr_hiv_att_5km_significant_10pct\": hiv5[\"stars\"] >= 1,\n \"dcr_att_5km_ci_low\": ci_lo, \"dcr_att_5km_ci_high\": ci_hi,\n \"ri_wage_ate\": w_ate, \"ri_wage_ate_ci_low\": w_lo, \"ri_wage_ate_ci_high\": w_hi,\n \"ri_wage_ate_se_reported\": w_se_printed,\n \"ri_wage_ate_se_derived\": w_se,\n \"ri_control_mean_wage\": ctrl_mean,\n \"ri_reemployment_ate_pp\": r_ate,\n \"ri_weeks_ui_ate\": u_ate, \"ri_weeks_ui_ate_ci_low\": u_lo, \"ri_weeks_ui_ate_ci_high\": u_hi,\n \"locators\": loc,\n \"derivation_notes\": {\n  \"dcr_att_5km_ci\": \"Normal (Wald) 95% CI: ATT +/- 1.96 x SE using Table 6 printed ATT -0.431 and SE 0.210 (clustered SE, large-sample normal approximation); rounded to 4 decimals.\",\n  \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using Table 2 printed 95% CI [166, 2,141]; assumes a symmetric normal-based CI (printed CI is symmetric around 1,153 to within rounding: 987 vs 988); rounded to 2 decimals. Using 2.0 instead would give 493.75.\"},\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": issues,\n \"n_input_rows\": {\"README.md\": wc_l(\"README.md\"),\n                  f\"sources/{DCR_ID}.txt\": wc_l(f\"sources/{DCR_ID}.txt\"),\n                  f\"sources/{RI_ID}.txt\": wc_l(f\"sources/{RI_ID}.txt\")},\n \"n_analysed\": 25,\n \"random_seed\": None,\n}\n# consistency diagnostics (printed only)\nimport math\nzw = w_ate / w_se\nprint(f\"Check: wage ATE z={zw:.3f}, implied two-sided normal p={math.erfc(abs(zw)/math.sqrt(2)):.4f} (printed p=0.022)\")\nprint(f\"Check: DCR 5km |t|={abs(a5['value'])/a5['se']:.3f} (printed **, i.e. p<0.05)\")\nprint(f\"Check: HIV 5km |t|={abs(hiv5['value'])/hiv5['se']:.3f} (no star)\")\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, ensure_ascii=False)\nprint(json.dumps({k: v for k, v in res.items() if k not 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace(''' \"random_seed\": None,\n}''',''' \"random_seed\": None,\n}\n# printed strings (JSON numbers drop trailing zeros, e.g. 0.210 -> 0.21)\nres[\"printed_values\"] = {\n \"dcr_att_5km\": a5[\"raw\"], \"dcr_att_5km_se\": \"(\" + a5[\"se_raw\"] + \")\",\n \"dcr_att_3km\": a3[\"raw\"], \"dcr_att_3km_se\": \"(\" + a3[\"se_raw\"] + \")\",\n \"dcr_obs_5km\": obs5[\"raw\"],\n \"dcr_narcotics_att_3km\": narc3[\"raw\"], \"dcr_narcotics_att_3km_se\": \"(\" + narc3[\"se_raw\"] + \")\",\n \"dcr_fatal_att_1km\": fat1[\"raw\"], \"dcr_fatal_att_1km_se\": \"(\" + fat1[\"se_raw\"] + \")\",\n \"dcr_hiv_att_5km\": hiv5[\"raw\"], \"dcr_hiv_att_5km_se\": \"(\" + hiv5[\"se_raw\"] + \")\",\n \"ri_wage_cell\": w[ATE], \"ri_control_wage_cell\": w[\"Control\"],\n \"ri_reemployment_cell\": r[ATE], \"ri_weeks_ui_cell\": u[ATE],\n}''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py > /dev/null 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 \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}\n  \"baseline_dcr_att_5km_ci_2se\": [\n    -0.851,\n    -0.011\n  ],\n  \"baseline_ri_wage_ate_se_2se\": 493.75,\n  \"printed_values\": {\n    \"dcr_att_5km\": \"-0.431**\",\n    \"dcr_att_5km_se\": \"(0.210)\",","is_error":false}]},"uuid":"8e5af31d-decc-4488-8a00-4ac30aaba3bb","timestamp":"2026-10-30T05:24:19.793Z","toolUseResult":{"stdout":"  \"ri_weeks_ui_cell\": \"-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\"\n }\n}\n  \"baseline_dcr_att_5km_ci_2se\": [\n    -0.851,\n    -0.011\n  ],\n  \"baseline_ri_wage_ate_se_2se\": 493.75,\n  \"printed_values\": {\n    \"dcr_att_5km\": \"-0.431**\",\n    \"dcr_att_5km_se\": 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('# printed strings','''# baseline: cruder \"2-sigma\" convention for the two derived quantities (sensitivity reference)\nres[\"baseline_dcr_att_5km_ci_2se\"] = [round(a5[\"value\"] - 2.0 * a5[\"se\"], 4), round(a5[\"value\"] + 2.0 * a5[\"se\"], 4)]\nres[\"baseline_ri_wage_ate_se_2se\"] = round((w_hi - w_lo) / 4.0, 2)\n# printed strings''',1)\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py | tail -3; grep -A3 baseline out/results.json","description":"Add baseline keys"}}]}}67{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"76eb0a68-1669-4a0e-8512-1e7a09c1de97","timestamp":"2026-10-30T05:24:19.796Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"b03cc2ff-295e-4da2-9bb0-7641f5c547d6","keep":18,"tail":["393e79eda5ab91c9afef58f0636df50b8ad9f9ef679068e9e128291d11d4aade","0c2d359ce646527d3ffa10dfd4fcd52394d7ac85c33c399ed6de5bbaee358d02"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f2a16f8ce7cfd54000e18de8b432db8b3fc3cdb70c137187ccbbf591af53574e"}68{"parentUuid":"8e5af31d-decc-4488-8a00-4ac30aaba3bb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs5A1cjBYpk4b2axguZ","type":"message","role":"assistant","content":[{"type":"text","text":"Now checks, state updates, then the gate."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":725,"cache_read_input_tokens":33649,"output_tokens":1181,"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":725},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs59kFHfZ3gtjFMYmtr","requestRef":"76eb0a68-1669-4a0e-8512-1e7a09c1de97","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"98616ba3-01bb-4b00-9a84-2c3a64ca12a5","timestamp":"2026-10-30T05:24:21.260Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"98616ba3-01bb-4b00-9a84-2c3a64ca12a5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs5A1cjBYpk4b2axguZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NPF343SkvJJ2WYSxYYGyhG","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json'))\nchk=[]\ndef c(name,cond): chk.append((name,bool(cond)))\nfor k in [\"dcr_att_5km_se\",\"dcr_att_3km_se\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km_se\",\"ri_wage_ate_se_derived\"]: c(f\"{k} > 0\", r[k]>0)\nc(\"DCR CI contains estimate\", r[\"dcr_att_5km_ci_low\"]<r[\"dcr_att_5km\"]<r[\"dcr_att_5km_ci_high\"])\nc(\"DCR CI symmetric about estimate\", abs((r[\"dcr_att_5km_ci_high\"]-r[\"dcr_att_5km\"])-(r[\"dcr_att_5km\"]-r[\"dcr_att_5km_ci_low\"]))<1e-9)\nc(\"DCR CI excludes 0 consistent with ** (p<0.05)\", r[\"dcr_att_5km_ci_high\"]<0)\nc(\"Wage CI contains ATE\", r[\"ri_wage_ate_ci_low\"]<r[\"ri_wage_ate\"]<r[\"ri_wage_ate_ci_high\"])\nc(\"Weeks CI contains ATE and lo<hi\", r[\"ri_weeks_ui_ate_ci_low\"]<r[\"ri_weeks_ui_ate\"]<r[\"ri_weeks_ui_ate_ci_high\"])\nc(\"HIV 5km |t|<1.645 consistent with no star\", abs(r[\"dcr_hiv_att_5km\"]/r[\"dcr_hiv_att_5km_se\"])<1.645 and r[\"dcr_hiv_att_5km_significant_10pct\"] is False)\nc(\"Every non-boolean value key has a locator\", all(k in r[\"locators\"] for k in r if k.startswith((\"dcr_\",\"ri_\")) and not isinstance(r[k],bool)))\nc(\"n_input_rows matches wc -l (10,134,58)\", list(r[\"n_input_rows\"].values())==[10,134,58])\nc(\"baseline 2.0 CI wider than 1.96 CI\", r[\"baseline_dcr_att_5km_ci_2se\"][0]<r[\"dcr_att_5km_ci_low\"])\nc(\"exclusions: 25-0=25=n_analysed\", r[\"n_analysed\"]==25)\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# P4 checks\\n\\n- Clean re-run of out/analysis.py after deleting out/results.json: byte-identical (cmp) -> PASS\\n\")\n    f.write(\"- Wage ATE: z = 1153/503.83 = 2.288, normal two-sided p = 0.0221 vs printed p=0.022 -> PASS (supports 1.96 convention)\\n\")\n    f.write(\"- Printed-string audit: printed_values in results.json reproduce source cells verbatim (incl. trailing zeros 0.210, -0.340) -> PASS\\n\")\n    for n,ok in chk: f.write(f\"- {n} -> {'PASS' if ok else 'FAIL'}\\n\")\nprint(chk)\nEOF","description":"Run consistency checks and write checks.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":725,"cache_read_input_tokens":33649,"output_tokens":1181,"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":725},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs59kFHfZ3gtjFMYmtr","requestRef":"76eb0a68-1669-4a0e-8512-1e7a09c1de97","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c4b2a8e3-f9fe-4078-a94f-5cb366146b16","timestamp":"2026-10-30T05:24:29.818Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"c4b2a8e3-f9fe-4078-a94f-5cb366146b16","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NPF343SkvJJ2WYSxYYGyhG","type":"tool_result","content":"[('dcr_att_5km_se > 0', True), ('dcr_att_3km_se > 0', True), ('dcr_narcotics_att_3km_se > 0', True), ('dcr_fatal_att_1km_se > 0', True), ('dcr_hiv_att_5km_se > 0', True), ('ri_wage_ate_se_derived > 0', True), ('DCR CI contains estimate', True), ('DCR CI symmetric about estimate', True), ('DCR CI excludes 0 consistent with ** (p<0.05)', True), ('Wage CI contains ATE', True), ('Weeks CI contains ATE and lo<hi', True), ('HIV 5km |t|<1.645 consistent with no star', True), ('Every non-boolean value key has a locator', True), ('n_input_rows matches wc -l (10,134,58)', True), ('baseline 2.0 CI wider than 1.96 CI', True), ('exclusions: 25-0=25=n_analysed', True)]","is_error":false}]},"uuid":"862f8398-e66b-4154-9d50-f627c9aca0d9","timestamp":"2026-10-30T05:24:29.882Z","toolUseResult":{"stdout":"[('dcr_att_5km_se > 0', True), ('dcr_att_3km_se > 0', True), ('dcr_narcotics_att_3km_se > 0', True), ('dcr_fatal_att_1km_se > 0', True), ('dcr_hiv_att_5km_se > 0', True), ('ri_wage_ate_se_derived > 0', True), ('DCR CI contains estimate', True), ('DCR CI symmetric about estimate', True), ('DCR CI excludes 0 consistent with ** (p<0.05)', True), ('Wage CI contains ATE', True), ('Weeks CI contains ATE and lo<hi', True), ('HIV 5km |t|<1.645 consistent with no star', True), ('Every non-boolean value key has a locator', True), ('n_input_rows matches wc -l (10,134,58)', True), ('baseline 2.0 CI wider than 1.96 CI', True), ('exclusions: 25-0=25=n_analysed', True)]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c4b2a8e3-f9fe-4078-a94f-5cb366146b16","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"104cccd3613248ebfea92310ad5641484b92c223366eec8b5ead30eec0bdf409","message":{"role":"assistant","content":[{"type":"text","text":"Now checks, state updates, then the gate."},{"type":"tool_use","id":"toolu_01NPF343SkvJJ2WYSxYYGyhG","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json'))\nchk=[]\ndef c(name,cond): chk.append((name,bool(cond)))\nfor k in [\"dcr_att_5km_se\",\"dcr_att_3km_se\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km_se\",\"ri_wage_ate_se_derived\"]: c(f\"{k} > 0\", r[k]>0)\nc(\"DCR CI contains estimate\", r[\"dcr_att_5km_ci_low\"]<r[\"dcr_att_5km\"]<r[\"dcr_att_5km_ci_high\"])\nc(\"DCR CI symmetric about estimate\", abs((r[\"dcr_att_5km_ci_high\"]-r[\"dcr_att_5km\"])-(r[\"dcr_att_5km\"]-r[\"dcr_att_5km_ci_low\"]))<1e-9)\nc(\"DCR CI excludes 0 consistent with ** (p<0.05)\", r[\"dcr_att_5km_ci_high\"]<0)\nc(\"Wage CI contains ATE\", r[\"ri_wage_ate_ci_low\"]<r[\"ri_wage_ate\"]<r[\"ri_wage_ate_ci_high\"])\nc(\"Weeks CI contains ATE and lo<hi\", r[\"ri_weeks_ui_ate_ci_low\"]<r[\"ri_weeks_ui_ate\"]<r[\"ri_weeks_ui_ate_ci_high\"])\nc(\"HIV 5km |t|<1.645 consistent with no star\", abs(r[\"dcr_hiv_att_5km\"]/r[\"dcr_hiv_att_5km_se\"])<1.645 and r[\"dcr_hiv_att_5km_significant_10pct\"] is False)\nc(\"Every non-boolean value key has a locator\", all(k in r[\"locators\"] for k in r if k.startswith((\"dcr_\",\"ri_\")) and not isinstance(r[k],bool)))\nc(\"n_input_rows matches wc -l (10,134,58)\", list(r[\"n_input_rows\"].values())==[10,134,58])\nc(\"baseline 2.0 CI wider than 1.96 CI\", r[\"baseline_dcr_att_5km_ci_2se\"][0]<r[\"dcr_att_5km_ci_low\"])\nc(\"exclusions: 25-0=25=n_analysed\", r[\"n_analysed\"]==25)\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# P4 checks\\n\\n- Clean re-run of out/analysis.py after deleting out/results.json: byte-identical (cmp) -> PASS\\n\")\n    f.write(\"- Wage ATE: z = 1153/503.83 = 2.288, normal two-sided p = 0.0221 vs printed p=0.022 -> PASS (supports 1.96 convention)\\n\")\n    f.write(\"- Printed-string audit: printed_values in results.json reproduce source cells verbatim (incl. trailing zeros 0.210, -0.340) -> PASS\\n\")\n    for n,ok in chk: f.write(f\"- {n} -> {'PASS' if ok else 'FAIL'}\\n\")\nprint(chk)\nEOF","description":"Run consistency checks and write checks.md"}}]}}72{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"ed3ff559-b301-405b-8f16-120373d0594f","timestamp":"2026-10-30T05:24:29.886Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"76eb0a68-1669-4a0e-8512-1e7a09c1de97","keep":20,"tail":["104cccd3613248ebfea92310ad5641484b92c223366eec8b5ead30eec0bdf409","a2f9fa28f360b801a990407e01bf3f88d87fda4cfb58580114e5c1222ee4d529"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3cd188893c4ed514e89eb63b15762f373bbc5e402086a5d10dab24d99a0ace4e"}73{"parentUuid":"862f8398-e66b-4154-9d50-f627c9aca0d9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs5u8wqLBvgKqqArCCt","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":1524,"cache_read_input_tokens":34374,"output_tokens":4376,"output_tokens_details":{"thinking_tokens":70},"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":1524},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":779,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs5tsaJ4YRHb7G1s47G","requestRef":"ed3ff559-b301-405b-8f16-120373d0594f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"437f601d-cef3-47c6-ad96-1e0cc47a9058","timestamp":"2026-10-30T05:24:32.794Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"437f601d-cef3-47c6-ad96-1e0cc47a9058","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs5u8wqLBvgKqqArCCt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DfroqrrvjjMHTGw2iG9N7z","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nI extracted specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts **exactly as printed** in two source extracts. The first is Armendariz (2026), arXiv:2609.27732v1: Swiss drug consumption rooms (DCR), staggered difference-in-differences, Tables 6–7. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1: Rhode Island RESEA randomised trial, Table 2. Each value has a locator. I also derived two quantities the sources do not print (a 95% CI from an SE, and an SE from a 95% CI) and identified which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nInputs are `data/README.md` (10 lines) and the two extracts (134 and 58 lines) (`n_input_rows`). Their SHA-256 hashes match the README (`out/state/data_audit.md`). I excluded nothing: all 25 requested value/boolean items were assessed (`n_analysed`; `out/exclusions.csv`). The inputs contain internal inconsistencies, listed in `data_issues`. None of them changes a requested cell, because each key names its table:\n- Table 6 prints the 5 km SE as (0.210). Appendix Table A19 prints (0.218) for the same estimate, −0.431**. I used Table 6.\n- The tables say there are 608 control regions and the A19 note says 640. The Table 6 observation counts equal 25 × (640 + treated regions), which fits 640.\n- Some prose disagrees with the tables: the abstract says fatal hospitalisations fall by \"35%\" against a Table 7 5 km estimate of −0.354 log points, about −29.8%; the HIV estimates are called \"close to zero\"; and the weeks-on-UI CI is given as magnitudes. I used the table values throughout.\n\n## Method\n`out/analysis.py` parses the table blocks of each extract (cells separated by \" | \"; in the DCR tables the SEs sit on the line after each coefficient). It prints every matched source line and writes `out/results.json`. The verbatim cell strings are kept under `printed_values`, because JSON numbers drop trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34, with numeric value unchanged. Thousands separators and \"$\" are removed (17,250 → 17250). Significance comes from the stars. The script searches the DCR extract for exact \"p=\" values and finds none. No randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study, Table 6** (log points):\n- 5 km ATT: `dcr_att_5km` = −0.431, printed with ** (p < 0.05); SE `dcr_att_5km_se` = 0.210.\n- 3 km ATT: `dcr_att_3km` = −0.283; SE `dcr_att_3km_se` = 0.279.\n- 5 km observations: `dcr_obs_5km` = 17250.\n\n**DCR study, Table 7** (log points):\n- Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549***, SE `dcr_narcotics_att_3km_se` = 0.187.\n- Fatal drug-related, 1 km: `dcr_fatal_att_1km` = −1.207***, SE `dcr_fatal_att_1km_se` = 0.137.\n- HIV, 5 km: `dcr_hiv_att_5km` = −0.340, SE `dcr_hiv_att_5km_se` = 0.294. This cell has no star, so `dcr_hiv_att_5km_significant_10pct` = false. The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n\n**Derived:** the 95% CI for the 5 km ATT is [`dcr_att_5km_ci_low` = −0.8426, `dcr_att_5km_ci_high` = −0.0194].\n\n**RESEA study, Table 2:**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- The source prints no SE for this ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage: `ri_control_mean_wage` = 33043.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE: `ri_weeks_ui_ate` = −1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = −2.24, `ri_weeks_ui_ate_ci_high` = −1.75].\n\nEvery key has a locator in `locators`. Feasibility is `feasibility` = \"complete\".\n\n## Uncertainty\n- **DCR (Tables 6, 7):** the reported uncertainties are **standard errors** clustered at the MedStat region level, printed in parentheses, plus significance stars. No CIs or exact p-values are printed.\n- **RESEA (Table 2):** the reported uncertainties are **95% confidence intervals** and two-sided p-values. No SEs are printed.\n- **Derived DCR CI:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`). This assumes the estimator is approximately normal in large samples and that the printed SE is correct. Using 2.0 instead gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_2se`). Under either convention the interval excludes 0, which agrees with the ** printed in the table.\n- **Derived wage SE:** (upper − lower)/(2 × 1.96) (`derivation_notes.ri_wage_ate_se`). This assumes a symmetric, normal-based CI; the printed CI is symmetric to within rounding (987 vs 988 on each side). Using 2.0 gives 493.75 (`baseline_ri_wage_ate_se_2se`). As a check, the derived SE gives z = 2.288 and a normal two-sided p = 0.0221, which matches the printed p = 0.022 (`out/state/checks.md`).\n- **Rounding:** the derived values inherit the rounding of the printed inputs (3 decimals for the DCR SE, whole dollars for the CI bounds). Their last reported digits are therefore not precise.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not in the extract, so it is null. It could be approximated from the estimate and SE, but that needs a distributional assumption, and the task asks for the printed p-value only.\n- Table 6 and Appendix A19 give different SEs for the same 5 km estimate (0.210 vs 0.218), and the count of 608 control regions conflicts with the 640 implied by the A19 note and the observation counts. These are inconsistencies in the source that I cannot resolve. The 5 km CI would be slightly wider if 0.218 were correct (not computed, because the key names Table 6).\n- JSON cannot keep trailing zeros, so the printed form is given in `printed_values`.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431** with SE (0.210), a 3 km ATT of -0.283 with SE (0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix A19 prints SE 0.218 for the same estimate; Table 6 used as the key specifies.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137), HIV 5 km -0.340 (0.294) without a star.\",\"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\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"JSON stores -0.340 as -0.34; printed string kept in printed_values.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in arxiv-2609.27732v1 (stars only).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"Could be approximated under a normal assumption but is not printed.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under the normal 1.96 convention is [-0.8426, -0.0194] and excludes zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes asymptotic normality; inherits rounding of the printed SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], control mean $33,043, reemployment ATE 1.47 pp, and weeks-on-UI ATE -1.99 with CI [-2.24, -1.75]; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\",\"ri_wage_ate_se_reported\"],\"status\":\"supported\",\"caveat\":\"Prose gives the weeks CI as magnitudes; Table 2 values used.\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI (1.96 convention) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes symmetric normal-based CI; 2.0 convention gives 493.75.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed; source hashes match README\",\"line counts 10/134/58 via wc -l\",\"6 anomalies recorded in data_audit.md\",\"exclusions.csv: 25-0=25\"],\"notes\":\"No availability violations.\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_dcr_att_5km_ci_2se and baseline_ri_wage_ate_se_2se computed (2.0 convention)\"],\"notes\":\"Extraction task; baseline = cruder 2-sigma convention for derived keys.\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py parses tables and prints matched source lines\",\"results.json written\"],\"notes\":\"No randomness; random_seed null.\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"16 consistency checks PASS (checks.md)\",\"wage p-value consistency 0.0221 vs 0.022\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\nprintf -- \"- 2026-10-30 P2 passed (2.0-convention baseline).\\n- 2026-10-30 P3 passed: analysis.py written.\\n- 2026-10-30 P4 checks pass; running gate.\\n\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, phases; run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1524,"cache_read_input_tokens":34374,"output_tokens":4376,"output_tokens_details":{"thinking_tokens":70},"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":1524},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs5tsaJ4YRHb7G1s47G","requestRef":"ed3ff559-b301-405b-8f16-120373d0594f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2e9c769e-ce08-44da-b058-0268726c3cbe","timestamp":"2026-10-30T05:25:05.716Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}75{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2e9c769e-ce08-44da-b058-0268726c3cbe","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}76{"type":"atis-latch","atis":"","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}77{"parentUuid":"2e9c769e-ce08-44da-b058-0268726c3cbe","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 6 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0281/out/state/gate_receipts/20261030T052505.833956Z.json","is_error":true,"tool_use_id":"toolu_01DfroqrrvjjMHTGw2iG9N7z"}]},"uuid":"eec2ffe7-de03-4253-9d9a-bedadb5acfef","timestamp":"2026-10-30T05:25:05.842Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 48 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\n  [ok] results_json: parsed\n  [ok] key:dcr_att_5km: ok\n  [ok] key:dcr_att_5km_se: ok\n  [ok] key:dcr_att_3km: ok\n  [ok] key:dcr_att_3km_se: ok\n  [ok] key:dcr_obs_5km: ok\n  [ok] key:dcr_narcotics_att_3km: ok\n  [ok] key:dcr_narcotics_att_3km_se: ok\n  [ok] key:dcr_fatal_att_1km: ok\n  [ok] key:dcr_fatal_att_1km_se: ok\n  [ok] key:dcr_hiv_att_5km: ok\n  [ok] key:dcr_hiv_att_5km_se: ok\n  [ok] key:ri_wage_ate: ok\n  [ok] key:ri_wage_ate_ci_low: ok\n  [ok] key:ri_wage_ate_ci_high: ok\n  [ok] key:ri_control_mean_wage: ok\n  [ok] key:ri_reemployment_ate_pp: ok\n  [ok] key:ri_weeks_ui_ate: ok\n  [ok] key:ri_weeks_ui_ate_ci_low: ok\n  [ok] key:ri_weeks_ui_ate_ci_high: ok\n  [ok] key:dcr_att_5km_ci_low: ok\n  [ok] key:dcr_att_5km_ci_high: ok\n  [ok] key:ri_wage_ate_se_derived: ok\n  [ok] key:dcr_hiv_att_5km_p_value: ok\n  [ok] key:dcr_hiv_att_5km_significant_10pct: ok\n  [ok] key:ri_wage_ate_se_reported: ok\n  [ok] key:locators: ok\n  [ok] key:derivation_notes: ok\n  [ok] key:task_id: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 6 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0281/out/state/gate_receipts/20261030T052505.833956Z.json","sandboxed":false,"sourceToolAssistantUUID":"2e9c769e-ce08-44da-b058-0268726c3cbe","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}78{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"4bae5f4176b78b3cc18f20f379ee7aacb359e57825149ddb7934ebe413ac3221","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01DfroqrrvjjMHTGw2iG9N7z","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-01 — Treatment effects and uncertainty extracted from two published tables\n\n## Question\nI extracted specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and sample counts **exactly as printed** in two source extracts. The first is Armendariz (2026), arXiv:2609.27732v1: Swiss drug consumption rooms (DCR), staggered difference-in-differences, Tables 6–7. The second is Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1: Rhode Island RESEA randomised trial, Table 2. Each value has a locator. I also derived two quantities the sources do not print (a 95% CI from an SE, and an SE from a 95% CI) and identified which requested quantity is not reported at all. Nothing is re-estimated.\n\n## Data and exclusions\nInputs are `data/README.md` (10 lines) and the two extracts (134 and 58 lines) (`n_input_rows`). Their SHA-256 hashes match the README (`out/state/data_audit.md`). I excluded nothing: all 25 requested value/boolean items were assessed (`n_analysed`; `out/exclusions.csv`). The inputs contain internal inconsistencies, listed in `data_issues`. None of them changes a requested cell, because each key names its table:\n- Table 6 prints the 5 km SE as (0.210). Appendix Table A19 prints (0.218) for the same estimate, −0.431**. I used Table 6.\n- The tables say there are 608 control regions and the A19 note says 640. The Table 6 observation counts equal 25 × (640 + treated regions), which fits 640.\n- Some prose disagrees with the tables: the abstract says fatal hospitalisations fall by \"35%\" against a Table 7 5 km estimate of −0.354 log points, about −29.8%; the HIV estimates are called \"close to zero\"; and the weeks-on-UI CI is given as magnitudes. I used the table values throughout.\n\n## Method\n`out/analysis.py` parses the table blocks of each extract (cells separated by \" | \"; in the DCR tables the SEs sit on the line after each coefficient). It prints every matched source line and writes `out/results.json`. The verbatim cell strings are kept under `printed_values`, because JSON numbers drop trailing zeros: 0.210 is stored as 0.21 and −0.340 as −0.34, with numeric value unchanged. Thousands separators and \"$\" are removed (17,250 → 17250). Significance comes from the stars. The script searches the DCR extract for exact \"p=\" values and finds none. No randomness is involved (`random_seed` = null).\n\n## Results\n**DCR study, Table 6** (log points):\n- 5 km ATT: `dcr_att_5km` = −0.431, printed with ** (p < 0.05); SE `dcr_att_5km_se` = 0.210.\n- 3 km ATT: `dcr_att_3km` = −0.283; SE `dcr_att_3km_se` = 0.279.\n- 5 km observations: `dcr_obs_5km` = 17250.\n\n**DCR study, Table 7** (log points):\n- Narcotics, 3 km: `dcr_narcotics_att_3km` = 0.549***, SE `dcr_narcotics_att_3km_se` = 0.187.\n- Fatal drug-related, 1 km: `dcr_fatal_att_1km` = −1.207***, SE `dcr_fatal_att_1km_se` = 0.137.\n- HIV, 5 km: `dcr_hiv_att_5km` = −0.340, SE `dcr_hiv_att_5km_se` = 0.294. This cell has no star, so `dcr_hiv_att_5km_significant_10pct` = false. The source prints no exact p-value, so `dcr_hiv_att_5km_p_value` = null.\n\n**Derived:** the 95% CI for the 5 km ATT is [`dcr_att_5km_ci_low` = −0.8426, `dcr_att_5km_ci_high` = −0.0194].\n\n**RESEA study, Table 2:**\n- Wage ATE: `ri_wage_ate` = 1153 USD/yr, 95% CI [`ri_wage_ate_ci_low` = 166, `ri_wage_ate_ci_high` = 2141].\n- The source prints no SE for this ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is `ri_wage_ate_se_derived` = 503.83.\n- Control-group mean wage: `ri_control_mean_wage` = 33043.\n- Reemployment ATE: `ri_reemployment_ate_pp` = 1.47 pp.\n- Weeks on UI ATE: `ri_weeks_ui_ate` = −1.99 weeks, 95% CI [`ri_weeks_ui_ate_ci_low` = −2.24, `ri_weeks_ui_ate_ci_high` = −1.75].\n\nEvery key has a locator in `locators`. Feasibility is `feasibility` = \"complete\".\n\n## Uncertainty\n- **DCR (Tables 6, 7):** the reported uncertainties are **standard errors** clustered at the MedStat region level, printed in parentheses, plus significance stars. No CIs or exact p-values are printed.\n- **RESEA (Table 2):** the reported uncertainties are **95% confidence intervals** and two-sided p-values. No SEs are printed.\n- **Derived DCR CI:** ATT ± 1.96 × SE (`derivation_notes.dcr_att_5km_ci`). This assumes the estimator is approximately normal in large samples and that the printed SE is correct. Using 2.0 instead gives [−0.851, −0.011] (`baseline_dcr_att_5km_ci_2se`). Under either convention the interval excludes 0, which agrees with the ** printed in the table.\n- **Derived wage SE:** (upper − lower)/(2 × 1.96) (`derivation_notes.ri_wage_ate_se`). This assumes a symmetric, normal-based CI; the printed CI is symmetric to within rounding (987 vs 988 on each side). Using 2.0 gives 493.75 (`baseline_ri_wage_ate_se_2se`). As a check, the derived SE gives z = 2.288 and a normal two-sided p = 0.0221, which matches the printed p = 0.022 (`out/state/checks.md`).\n- **Rounding:** the derived values inherit the rounding of the printed inputs (3 decimals for the DCR SE, whole dollars for the CI bounds). Their last reported digits are therefore not precise.\n\n## Limitations and open issues\n- The exact p-value of the HIV 5 km ATT is not in the extract, so it is null. It could be approximated from the estimate and SE, but that needs a distributional assumption, and the task asks for the printed p-value only.\n- Table 6 and Appendix A19 give different SEs for the same 5 km estimate (0.210 vs 0.218), and the count of 608 control regions conflicts with the 640 implied by the A19 note and the observation counts. These are inconsistencies in the source that I cannot resolve. The 5 km CI would be slightly wider if 0.218 were correct (not computed, because the key names Table 6).\n- JSON cannot keep trailing zeros, so the printed form is given in `printed_values`.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431** with SE (0.210), a 3 km ATT of -0.283 with SE (0.279), and 17,250 observations at 5 km.\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_att_3km\",\"dcr_att_3km_se\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"Appendix A19 prints SE 0.218 for the same estimate; Table 6 used as the key specifies.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints narcotics 3 km 0.549*** (0.187), fatal 1 km -1.207*** (0.137), HIV 5 km -0.340 (0.294) without a star.\",\"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\",\"dcr_hiv_att_5km_significant_10pct\"],\"status\":\"supported\",\"caveat\":\"JSON stores -0.340 as -0.34; printed string kept in printed_values.\"},\n {\"id\":\"C3\",\"text\":\"The exact p-value of the HIV 5 km ATT is not reported in arxiv-2609.27732v1 (stars only).\",\"results_keys\":[\"dcr_hiv_att_5km_p_value\"],\"status\":\"supported\",\"caveat\":\"Could be approximated under a normal assumption but is not printed.\"},\n {\"id\":\"C4\",\"text\":\"Derived 95% CI for the Table 6 5 km ATT under the normal 1.96 convention is [-0.8426, -0.0194] and excludes zero.\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes asymptotic normality; inherits rounding of the printed SE.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1,153 with 95% CI [166, 2,141], control mean $33,043, reemployment ATE 1.47 pp, and weeks-on-UI ATE -1.99 with CI [-2.24, -1.75]; no SE is printed.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_control_mean_wage\",\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\",\"ri_wage_ate_se_reported\"],\"status\":\"supported\",\"caveat\":\"Prose gives the weeks CI as magnitudes; Table 2 values used.\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI (1.96 convention) is 503.83 USD/yr, consistent with the printed p=0.022.\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_2se\"],\"status\":\"supported\",\"caveat\":\"Assumes symmetric normal-based CI; 2.0 convention gives 493.75.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed; source hashes match README\",\"line counts 10/134/58 via wc -l\",\"6 anomalies recorded in data_audit.md\",\"exclusions.csv: 25-0=25\"],\"notes\":\"No availability violations.\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline_dcr_att_5km_ci_2se and baseline_ri_wage_ate_se_2se computed (2.0 convention)\"],\"notes\":\"Extraction task; baseline = cruder 2-sigma convention for derived keys.\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py parses tables and prints matched source lines\",\"results.json written\"],\"notes\":\"No randomness; random_seed null.\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical (cmp)\",\"16 consistency checks PASS (checks.md)\",\"wage p-value consistency 0.0221 vs 0.022\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\nprintf -- \"- 2026-10-30 P2 passed (2.0-convention baseline).\\n- 2026-10-30 P3 passed: analysis.py written.\\n- 2026-10-30 P4 checks pass; running gate.\\n\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, phases; run gate"}}]}}79{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"d196c2f5-0340-45b1-bbe9-6a4ed6dd0e60","timestamp":"2026-10-30T05:25:05.847Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"ed3ff559-b301-405b-8f16-120373d0594f","keep":22,"tail":["4bae5f4176b78b3cc18f20f379ee7aacb359e57825149ddb7934ebe413ac3221","836afde06b972e649e154c17b9416a0c577bcb690b6f43855c3bfbe42be3fd3b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"681ef76c9d1cf0cbec920c165e43ae41c9c060c79a25b2e89b57ec69311e53c6"}80{"parentUuid":"eec2ffe7-de03-4253-9d9a-bedadb5acfef","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs8YssrNDJbC7XpSwP6","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSgwcKEAgTGAI4AUIIdGhpbmtpbmcSDKwifXLrmsb1YK9N+xoMVCkYNgqi6AMKs+tOIjA9D2W0D5Cyk44pJhMYg7bL6f04EynnOAA1xEb4DGKOHDt2x+69OMq1nhTF9VOWufcqoAZ06xhTOo/p/JnM0UcyglY0nvd9VzeVjD5SadFKpCQe9JFkbvCfcLLJm1azMy2HDM3FNXu3U9s0Qgy7rmBt7AugdTLjfg2xg2G3BSOjeFyQzHJhlrqhDtyz5EZe5aZm75mtb9WLKooRudYq1Ynm86WAMsIf3dY/TDRSpR3RP2PWuf7/zU5c0rHhCLoSRB4W2WVRzyUly8xZ3mzdBnxqdnmTtzFF9qaU/KqWMGDyI5q8C2XxxDTrIUGgnniP4VZDxVdCBompj1LzEqh5acUB0m7I2lOQ5/t3ZTsYv5NMxd9g700tfV5ls5iOK8h6YMtqzd7lbF/5cAVVNw2mNtNVzrJHGCtfujxxQlvevnFQoeM4rH9DKzmukKomUXH/+Uwqrcrlye1gDcsoRvhCMjk0MNr/ganePVm7PmzGI6PhUwQUlC7jjWBJKkzanU+f73yrMuaoCyL3teZGlvfnbp9F3NAZ45arPlc5cqcl9l6m13GtQCM9budswgHWkKgbSTON7inIj8QlzxprE8740GCFM95C2L8AvAfzFkhiyCkqyKNLaZG2vRt0RjtyisNzTlexVRJt/TEGpPKUC+Z8HiS2SXmjsNYrI0w6vppIZn68VLZk7enXK8fWBakSia2EYme9zqqL9XH7M6ZM2VLM4dj2n+ZYTumC2Bs0A+U5jRccSuTkartbBX0tRa14cbtswGgxs8ICp3Gri+EvOlLwS+Oa2FsRhr+K7eWd4yuDk95o5ENSf8kdRf0bFrCTNmk3CbUlWl9DWvtPAcBhdraqyyyDq29Bn9YT3A7FaMToB7E6SDGwlC/Z2lMYF5vaGlgl38FrYyPhRl31+/84gIVQUktuivxkKs8zwainDekvxacHB37nUQ9iqhydous/1pVbu/SIqBT5aD1CxvSL3k0GL8cTsb+BmiVQ81SznB3v1joEjQvnYDFWU7e8b9hcfNsh56IZqD5GktXUwqJ2SMVy5vOTQx+yR37xGYrlc+puuiWoL5rmc4LhuOOf37jBj8fiNtKcugsvmYqLBpm4Oc0GaoEGjsgGFYi3SGCAfhgEdi9Dm1fRyxgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5462,"cache_read_input_tokens":35898,"output_tokens":530,"output_tokens_details":{"thinking_tokens":49},"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":5462},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":702,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXs8YcFTDveQdhyDHsau","requestRef":"d196c2f5-0340-45b1-bbe9-6a4ed6dd0e60","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8722dd04-c3f6-4752-84cb-adf69393517a","timestamp":"2026-10-30T05:25:08.345Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}81{"parentUuid":"8722dd04-c3f6-4752-84cb-adf69393517a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs8YssrNDJbC7XpSwP6","type":"message","role":"assistant","content":[{"type":"text","text":"Only the phase-ledger check failed, and that was expected because P4 and P5 had not been finalised yet. I'm marking them passed now and recording the receipts."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5462,"cache_read_input_tokens":35898,"output_tokens":530,"output_tokens_details":{"thinking_tokens":49},"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":5462},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXs8YcFTDveQdhyDHsau","requestRef":"d196c2f5-0340-45b1-bbe9-6a4ed6dd0e60","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5353cf88-4199-4476-8b33-c2d247690be6","timestamp":"2026-10-30T05:25:08.346Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}82{"parentUuid":"5353cf88-4199-4476-8b33-c2d247690be6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs8YssrNDJbC7XpSwP6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Wg2vpDkM9qFACvtjStqRc7","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P4\"][\"status\"]=\"passed\"\np[\"P4\"][\"checks\"].append(\"gate run 1: 47/48 ok; 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identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0281/out/state/gate_receipts/20261030T052511.599190Z.json","is_error":false}]},"uuid":"344aa087-f98e-471c-867c-4636b1399fee","timestamp":"2026-10-30T05:25:11.607Z","toolUseResult":{"stdout":"  [ok] claims_json: 6 claims, all keys resolve\n  [ok] phase_ledger: consistent\n  [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0281/out/state/gate_receipts/20261030T052511.599190Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c2745eb6-6fe9-41bb-98fc-fa0d209b90ad","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}84{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"56244b3def6d9bd479f8761a7a8174f75aaaf15f7f28b07f2bbc72ca0c127a24","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Only the phase-ledger check failed, and that was expected because P4 and P5 had not been finalised yet. I'm marking them passed now and recording the receipts."},{"type":"tool_use","id":"toolu_01Wg2vpDkM9qFACvtjStqRc7","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P4\"][\"status\"]=\"passed\"\np[\"P4\"][\"checks\"].append(\"gate run 1: 47/48 ok; only failure = phase ledger non-terminal (expected mid-run); reproducibility ok; receipt out/state/gate_receipts/20261030T052505.833956Z.json\")\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, each number cites a results key\",\"claims.json 6 claims, all keys resolve\",\"final gate run: see log/receipts\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1 receipt 20261030T052505.833956Z); P5 report+claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -4","description":"Finalize phases and rerun gate"}}]}}85{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"bc00e8db-59a3-4fcc-aab7-f6dffd562438","timestamp":"2026-10-30T05:25:11.611Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"d196c2f5-0340-45b1-bbe9-6a4ed6dd0e60","keep":24,"tail":["56244b3def6d9bd479f8761a7a8174f75aaaf15f7f28b07f2bbc72ca0c127a24","f120854c355c177ac019be82ccaac112f02e1ce3728d0c8e64d41f0d1488946c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c66594a573d6b6abc015744e7c2cc9117b6d5c15b6a629b548ef27375cfc0d2c"}86{"parentUuid":"344aa087-f98e-471c-867c-4636b1399fee","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs8yduFS6u6jZ4oKZAY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01ASPj3oaFHGu8muwJKuHW5w","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P5\"][\"checks\"][-1]=\"final gate run: PASSED 48/48; receipt out/state/gate_receipts/20261030T052511.599190Z.json\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T052511.599190Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":658,"cache_read_input_tokens":41360,"output_tokens":299,"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":658},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs8yGaMKtPqyi1QiPTH","requestRef":"bc00e8db-59a3-4fcc-aab7-f6dffd562438","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"72e5ddba-a6f1-4c89-9e11-2ed39484b305","timestamp":"2026-10-30T05:25:15.124Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}87{"parentUuid":"72e5ddba-a6f1-4c89-9e11-2ed39484b305","isSidechain":false,"promptId":"7272a9ab-d5f6-4028-983a-34bd169d95f9","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01ASPj3oaFHGu8muwJKuHW5w","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)","is_error":false}]},"uuid":"9e25f75e-12a0-40e3-b34f-1aef84047b6c","timestamp":"2026-10-30T05:25:15.261Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"72e5ddba-a6f1-4c89-9e11-2ed39484b305","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}88{"type":"api-request-blob","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","hash":"1d9a894deb35ed6fe3785d62d1a17334e241631144b8960d1a7e6b10f8ccca30","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01ASPj3oaFHGu8muwJKuHW5w","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np[\"P5\"][\"checks\"][-1]=\"final gate run: PASSED 48/48; receipt out/state/gate_receipts/20261030T052511.599190Z.json\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T052511.599190Z). Task complete.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1","description":"Record final receipt and confirm gate"}}]}}89{"type":"api-request","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","id":"58aefa74-4b78-45f4-9578-88ef3f7ff5d3","timestamp":"2026-10-30T05:25:15.265Z","version":"2.1.314","querySource":"sdk","shapeHash":"18e089944e0b18ecd3dcacba134f98a72a1554da6952d487aefb2cb8b2cbb021","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":"bc00e8db-59a3-4fcc-aab7-f6dffd562438","keep":26,"tail":["1d9a894deb35ed6fe3785d62d1a17334e241631144b8960d1a7e6b10f8ccca30","fb0c3d6464e278d0e5352ee853d4f5c93b5acb043ce77a16ad1f8594432153fd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"258e9dfaf28149d3efc064ae8cba7c4510303b4ed63df6d7fa789a9e544f4295"}90{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9e25f75e-12a0-40e3-b34f-1aef84047b6c","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}91{"type":"atis-latch","atis":"","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}92{"parentUuid":"9e25f75e-12a0-40e3-b34f-1aef84047b6c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXs9FBnL6Razcc6pFnpM","type":"message","role":"assistant","content":[{"type":"text","text":"I extracted all the requested values from the two papers and derived the two quantities they don't print; the final gate passes all 48 checks. Re-running `out/analysis.py` from scratch reproduces `out/results.json` exactly, and the script prints each source line it matched.\n\n**Drug consumption room study (arxiv-2609.27732v1), in log points:**\n\n| Table | Item | Estimate | Standard error |\n|---|---|---|---|\n| 6 | ATT, 5 km | −0.431** | 0.210 |\n| 6 | ATT, 3 km | −0.283 | 0.279 |\n| 6 | Observations, 5 km | 17250 | — |\n| 7 | Narcotics, 3 km | 0.549*** | 0.187 |\n| 7 | Fatal, 1 km | −1.207*** | 0.137 |\n| 7 | HIV, 5 km | −0.340 | 0.294 |\n\n- **HIV 5 km p-value:** not reported (`null`). The table only gives stars, and this cell has none, so it is marked not significant at 10%.\n- **Derived 95% interval for the 5 km ATT:** [−0.8426, −0.0194], using estimate ± 1.96 × standard error. It excludes zero, which matches the ** in the table.\n\n**RESEA study (arxiv-2606.14621v1), Table 2:**\n- **Wage effect:** $1,153 per year, 95% interval [166, 2,141]. No standard error is printed.\n- **Derived wage standard error:** 503.83, from interval width ÷ (2 × 1.96). As a check, that standard error gives p ≈ 0.0221, matching the printed p = 0.022.\n- **Control-group mean wage:** $33,043.\n- **Reemployment effect:** 1.47 percentage points.\n- **Weeks on unemployment insurance:** −1.99 weeks, 95% interval [−2.24, −1.75].\n\n**Problems in the source papers** (listed in `data_issues`; none changes a requested value):\n- For the same 5 km estimate, Table 6 prints a standard error of 0.210 but Appendix Table A19 prints 0.218. I used Table 6, as the task specifies.\n- The tables say there are 608 control regions, but a note says 640. The observation counts work out to 25 × (640 + treated regions), which fits 640, not 608.\n- Some of the text contradicts the tables:\n  - The abstract says fatal hospitalisations fall \"35%\", but the 5 km estimate of −0.354 log points is about −29.8%.\n  - HIV estimates are described as \"close to zero\" although they are −0.239 and −0.340.\n  - The RESEA weeks interval is written in the text as positive numbers.\n\n**Limitation:** JSON drops trailing zeros, so 0.210 is stored as 0.21 and −0.340 as −0.34. The exact printed text is kept alongside under `printed_values`.\n\nEverything is in `out/`; open `report.md` first. The rest:\n- `results.json`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (scope notes, data audit, checks, phase log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":339,"cache_read_input_tokens":42018,"output_tokens":1059,"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":339},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXs9EtgGgsc9iVxvkpjx","requestRef":"58aefa74-4b78-45f4-9578-88ef3f7ff5d3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4f695f79-75d5-4bbe-ac34-b63cc3e630ad","timestamp":"2026-10-30T05:25:25.221Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0281","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","version":"2.1.314","gitBranch":"HEAD"}93{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4f695f79-75d5-4bbe-ac34-b63cc3e630ad","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf"}94{"type":"cost-state","sessionId":"56559128-64c3-4527-9c41-4021970c2ddf","totalCostUSD":0.6538503999999998,"totalAPIDuration":171835,"totalAPIDurationWithoutRetries":171826,"totalToolDuration":1205,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":173169,"startTime":1793337752060,"modelUsage":{"claude-opus-5-5":{"inputTokens":30,"outputTokens":18908,"thinkingTokens":1983,"cacheReadInputTokens":356527,"cacheCreationInputTokens":40853,"cacheCreationEphemeral5mInputTokens":40853,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.6538503999999998}},"hasUnknownModelCost":false}95