1{"type":"atis-latch","atis":"","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:27:13.044Z","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","content":"Review"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:27:13.049Z","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}4{"parentUuid":null,"isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":"Review"},"uuid":"205ef34a-919e-428b-a79c-1c241cb3e9fe","timestamp":"2026-10-30T05:27:13.053Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","shapeHash":"e75fa6184f54c325da01a326d15f9b377bc3c835f3f564e705d266156d0b4525","timestamp":"2026-10-30T05:27:13.066Z","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/RC0283\nDate: 2026-10-30\n\nYou are an independent scientific reviewer. In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. 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Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. 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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/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}12{"parentUuid":"e4b29e86-b66e-46a7-b199-b3de856d59cc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsHyN2TcCq37jgpNM1K","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BqyXnfZZeZWcc3rg8TkiNv","name":"Bash","input":{"command":"cat TASK.md; ls -R out; cat out/results.json; cat out/claims.json; cat out/report.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":716,"cache_read_input_tokens":1504,"output_tokens":84,"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":716},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsHy6A5KFhq4QvbViyf","requestRef":"0df11312-5b0b-46d4-8f35-25a5cd62240c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2504accd-ebc8-4110-ab81-1a2300d403bf","timestamp":"2026-10-30T05:27:15.128Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"2504accd-ebc8-4110-ab81-1a2300d403bf","isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BqyXnfZZeZWcc3rg8TkiNv","type":"tool_result","content":"# 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.\nanalysis.py\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nreview\nstate\n\nout/review:\n\nout/state:\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\n\nout/state/gate_receipts:\n20261030T052611.849624Z.json\n20261030T052657.688138Z.json\n20261030T052701.730727Z.json\n20261030T052712.843695Z.json\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.210,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.340,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_obs_5km\": 17250,\n \"dcr_hiv_att_5km_p_value\": null,\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\": 503.8265,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the coefficient, SE and significance stars; no exact p-value is printed anywhere in the extract)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (and SE line below) | column \\\"5 km\\\" | derived: estimate -/+ 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (and SE line below) | column \\\"5 km\\\" | derived: estimate -/+ 1.96 x SE\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | point estimate\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI lower bound\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI upper bound\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived: (CI upper - CI lower) / (2 x 1.96); SE not reported in arxiv-2606.14621v1\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" | mean (SD in parentheses not used)\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" | point estimate (pp)\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | point estimate\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI lower bound\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI upper bound\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"95% CI = ATT -/+ 1.96 x SE (normal approximation, critical value 1.96) using the printed Table 6 values ATT=-0.431, SE=0.210; rounded to 4 decimals. Table 6 does not print a CI. Sensitivity with critical value 2.0: see baseline_dcr_att_5km_ci_low_z2 / _high_z2.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using the printed Table 2 95% CI [166, 2,141] (normal approximation, critical value 1.96, assumes a symmetric Wald-type interval); rounded to 4 decimals. The source prints no SE.\"\n },\n \"task_id\": \"F4-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Same 5 km CS-DR ATT (-0.431) has SE 0.210 in Table 6 but SE 0.218 in Table A19 of arxiv-2609.27732v1.\",\n \"handling\": \"Keys name Table 6, so the Table 6 SE (0.210) is used for dcr_att_5km_se and the derived CI; the discrepancy is reported, not resolved.\"\n },\n {\n \"issue\": \"Control-region count inconsistent in arxiv-2609.27732v1: Tables 6/7 and Fig. 6 note say 608 never-treated regions, Table A19 note says 640. Table 6 observations divided by (treated + 640) = [25.0, 25.0, 25.0]; by (treated + 608) = [26.301, 26.244, 26.216].\",\n \"handling\": \"dcr_obs_5km reported as printed (17250); the arithmetic suggests the panel uses 640 controls x 25 periods, but this is not asserted as fact.\"\n },\n {\n \"issue\": \"Narrative of arxiv-2609.27732v1 is inconsistent with itself: abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 5 km ATT -0.354 -> exp(-0.354)-1 = -0.298); text calls HIV estimates 'close to zero' although 5 km ATT is -0.340 (not significant).\",\n \"handling\": \"Only table values are extracted; narrative claims are not used.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1 narrative reports the weeks-on-UI CI as unsigned magnitudes [1.75, 2.24] while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Signed Table 2 values used, lower bound first.\"\n },\n {\n \"issue\": \"Rendering defect in arxiv-2606.14621v1 extract line 39: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"No extracted key affected; ignored.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: Table 2 labels the column 'Average treatment effect estimate' while the Table 3 caption calls the same numbers ITT estimates; Table 2 caption does not state the CI construction beyond 'methodology described in the main text' (omitted from the extract).\",\n \"handling\": \"Values taken from Table 2 as required; SE derivation assumes a symmetric normal-approximation interval (critical value 1.96), which is an assumption.\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_low_z2\": -0.851,\n \"baseline_dcr_att_5km_ci_high_z2\": -0.011,\n \"baseline_ri_wage_ate_se_derived_z2\": 493.75,\n \"check_ri_wage_ci_midpoint\": 1153.5,\n \"check_ri_wage_p_implied_by_derived_se\": 0.0221,\n \"check_dcr_att_5km_p_implied_normal\": 0.0401,\n \"check_dcr_hiv_5km_abs_z\": 1.1565,\n \"check_dcr_a19_se_5km\": 0.218,\n \"check_table3_wage_identical_to_table2\": true\n}\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431 (**) with SE 0.210 and 17250 observations; the 3 km ATT is -0.283 (SE 0.279).\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_obs_5km\",\"dcr_att_3km\",\"dcr_att_3km_se\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same estimate; Table 6 used as the key requires.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km ATT 0.549 (SE 0.187), Drug-related (fatal) 1 km ATT -1.207 (SE 0.137), and HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and its exact p-value is not reported in the extract.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\",\"check_dcr_hiv_5km_abs_z\"],\"status\":\"supported\",\"caveat\":\"p-value set to null rather than derived.\"},\n {\"id\":\"C4\",\"text\":\"Using ATT +/- 1.96 x SE, the derived 95% CI of the Table 6 5 km ATT is [-0.8426, -0.0194]; with critical value 2.0 it is [-0.851, -0.011].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_low_z2\",\"baseline_dcr_att_5km_ci_high_z2\"],\"status\":\"supported\",\"caveat\":\"Derived under a normal approximation; not printed in the source.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1153 per year with 95% CI [166, 2141] and no standard error; control mean wage is $33043.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI with critical value 1.96 is 503.8265 (493.75 with 2.0), and it reproduces the printed p=0.022 (implied 0.0221).\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_derived_z2\",\"check_ri_wage_p_implied_by_derived_se\",\"check_ri_wage_ci_midpoint\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric normal-approximation CI; CI construction not described in the extract; CI bounds are rounded.\"},\n {\"id\":\"C7\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Narrative reports the weeks CI as unsigned magnitudes; signed Table 2 values used.\"},\n {\"id\":\"C8\",\"text\":\"The DCR extract is internally inconsistent about the control-region count (608 vs 640) and the 5 km SE (0.210 vs 0.218); observation counts equal 25 x (treated + 640).\",\"results_keys\":[\"data_issues\",\"check_dcr_a19_se_5km\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"The arithmetic is suggestive; which count is correct is not resolved by the extract.\"}\n]\n# F4-conf-01 — Extracting treatment effects and their uncertainty from two published tables\n\n## Question\nThe task is to pull specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and counts out of two arXiv extracts, copying them exactly as printed. The sources are Armendariz (2026), arXiv:2609.27732v1, a staggered DiD study of Swiss drug consumption rooms (DCRs), using Tables 6 and 7; and Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, an RCT of Rhode Island's RESEA programme, using Table 2. Each value gets a locator. I also derive two quantities the sources do not print, state the convention used for each, and flag requested quantities that are not reported. Nothing is re-estimated.\n\n## Data and exclusions\n- Inputs: `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) and `data/README.md` (10 lines) (`n_input_rows`). The SHA-256 of each extract matches the one listed in the README (`out/state/data_audit.md`).\n- Nothing was filtered out. All 25 requested value items were found and assessed (`n_analysed` = 25; `out/exclusions.csv` shows 0 removed).\n- Information rules: only the two extracts were used. Two other tables, DCR Table A19 and RESEA Table 3, also print some of the same numbers. I used them only for cross-checks, never as the source of a key.\n- Problems found in the inputs (`data_issues`, 6 entries):\n 1. Table 6 gives the 5 km CS-DR ATT (-0.431**) an SE of 0.210. Table A19 prints the same estimate with SE 0.218 (`check_dcr_a19_se_5km`). The key names Table 6, so I used Table 6.\n 2. The number of control regions is inconsistent: 608 in Tables 6 and 7, but 640 in the Table A19 note. The printed observation counts are exactly 25 × (treated + 640) and are not whole multiples of (treated + 608). I report the observation count as printed.\n 3. The DCR narrative contradicts itself. The abstract says fatal hospitalisations fall by 35%, while Section 6.1 says about 30% at 5 km. The text also calls the HIV estimates \"close to zero\", although the 5 km estimate is -0.340.\n 4. The RESEA narrative gives the weeks-on-UI CI as unsigned magnitudes. I used the signed values from Table 2.\n 5. There is a rendering defect, \"p ¡ 0.001\", in the RESEA text.\n 6. The RESEA tables label the same numbers differently: \"ATE\" in Table 2 and ITT in Table 3. The extract does not describe how the CIs were built.\n\n## Method\n`out/analysis.py` parses the \" | \"-separated table lines and attaches each SE line in parentheses to the coefficient row above it. It removes `$`, thousands separators, stars and units, keeps the sign, and writes each printed literal into the JSON so that trailing zeros survive (for example 0.210 and -0.340). It also prints every source line it matched. Significance at the 10% level is read from the stars: any of `*`, `**` or `***` means p < 0.10.\n\nThe two derived quantities use the normal critical value 1.96 (`derivation_notes`):\n- the DCR 95% CI is ATT ± 1.96 × SE;\n- the RESEA wage SE is (upper − lower)/(2 × 1.96).\n\nAs a sensitivity check I repeated both with a critical value of 2.0 (`baseline_*` keys). The script involves no randomness (`random_seed` = null).\n\n## Results\n**DCR study (arxiv-2609.27732v1), log points**\n\n| Quantity | Estimate | SE | Keys |\n|---|---|---|---|\n| Table 6 ATT, 5 km | -0.431 (\\*\\*) | 0.210 | `dcr_att_5km`, `dcr_att_5km_se` |\n| Table 6 ATT, 3 km | -0.283 | 0.279 | `dcr_att_3km`, `dcr_att_3km_se` |\n| Table 7 Narcotics, 3 km | 0.549 (\\*\\*\\*) | 0.187 | `dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se` |\n| Table 7 Drug-related (fatal), 1 km | -1.207 (\\*\\*\\*) | 0.137 | `dcr_fatal_att_1km`, `dcr_fatal_att_1km_se` |\n| Table 7 HIV, 5 km | -0.340 (no star) | 0.294 | `dcr_hiv_att_5km`, `dcr_hiv_att_5km_se` |\n\n- Table 6 reports 17250 observations for the 5 km buffer (`dcr_obs_5km`).\n- The source does not mark the HIV 5 km ATT as significant at the 10% level (`dcr_hiv_att_5km_significant_10pct` = false).\n- Its exact p-value is not printed, so it is reported as null (`dcr_hiv_att_5km_p_value`). The tables give significance stars only.\n- Derived 95% CI for the 5 km ATT: [-0.8426, -0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA study (arxiv-2606.14621v1), Table 2**\n- Wage ATE: $1153 per year, 95% CI [166, 2141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`).\n- No SE is printed for the wage ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is 503.8265 (`ri_wage_ate_se_derived`).\n- Control-group mean wage: $33043 (`ri_control_mean_wage`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks-on-UI ATE: -1.99 weeks, 95% CI [-2.24, -1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nEvery locator is in `locators`. Feasibility is \"complete\" (`feasibility`).\n\n## Uncertainty\n- **DCR values are standard errors.** In Tables 6 and 7 the numbers in parentheses are SEs clustered at the MedStat region level. The stars mark p < 0.01, 0.05 and 0.10.\n- **RESEA values are confidence intervals and p-values.** Table 2 prints two-sided p-values and 95% CIs, and no SEs.\n- **The derived DCR CI assumes normality.** It assumes the ATT is approximately normally distributed with the printed SE, and uses a critical value of 1.96. It is consistent with the source: it excludes 0, and the implied normal p is 0.0401 (`check_dcr_att_5km_p_implied_normal`), which fits the printed `**`. With a critical value of 2.0 the CI would be [-0.851, -0.011] (`baseline_dcr_att_5km_ci_low_z2`, `baseline_dcr_att_5km_ci_high_z2`).\n- **The derived wage SE assumes a symmetric normal-approximation CI.** It assumes the printed CI is estimate ± 1.96 × SE. Two checks support this:\n - the CI midpoint is 1153.5 (`check_ri_wage_ci_midpoint`);\n - the p-value implied by the derived SE is 0.0221 (`check_ri_wage_p_implied_by_derived_se`), close to the printed p = 0.022.\n\n With a critical value of 2.0 the SE would be 493.75 (`baseline_ri_wage_ate_se_derived_z2`). The extract does not describe how the CI was actually built. Because the CI bounds are rounded to whole dollars, the derived SE is only accurate to about ±0.3.\n- **The HIV 5 km estimate is not significant.** Its |ATT/SE| is 1.1565 (`check_dcr_hiv_5km_abs_z`), below 1.645, which matches the absence of a star.\n\n## Limitations and open issues\n- **The SE of the 5 km ATT is ambiguous.** Tables 6 and A19 print different SEs (0.210 vs 0.218) for the same estimate. I followed the key's definition and used Table 6, but the source itself is inconsistent. With 0.218 the derived CI would be wider.\n- **The number of control regions is uncertain.** The count is printed as 608, but 640 fits the observation counts. Which one is right is unresolved in the extract.\n- **The exact HIV p-value cannot be given.** It is not reported, so it is null. Recovering it from the SE would require a distributional assumption, and the key asks only for a printed value.\n- **The derived quantities rest on convention.** Both use the normal 1.96 critical value. A t critical value could not be used because the degrees of freedom are not reported (for DCR, the clusters are not stated consistently).\n- **The 3.5% wage figure was a check, not an extracted value.** The ratio of the wage ATE to the control mean matches the narrative's 3.5% (`out/state/checks.md`, check 15).","is_error":false}]},"uuid":"9704899b-38c4-4a6a-85d0-d8a95c22e8c9","timestamp":"2026-10-30T05:27:15.187Z","toolUseResult":{"stdout":"# F4-conf-01 — Extracting treatment effects and their uncertainty from two published results tables\n\n## Scientific question\nTwo open-access studies report treatment effects with uncertainty: a staggered difference-in-differences study of drug consumption room (DCR) openings in Switzerland (log hospitalisation rates; standard errors in parentheses) and a randomised evaluation of Rhode Island's RESEA programme (average treatment effects with 95% confidence intervals). The task is to extract specified point estimates, standard errors, confidence-interval bounds and sample counts **exactly as printed**, to attach to each value a locator (source id, table number, row label, column label), to derive two quantities that the sources do not print (a 95% confidence interval from a standard error, and a standard error from a confidence interval) while stating the convention used, and to recognise which requested quantity is not reported at all. The estimands are the printed numbers themselves; nothing is re-estimated.\n\n## Inputs\n- `data/sources/arxiv-2609.27732v1.txt` — extract of Armendariz (2026), *Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland*, arXiv:2609.27732v1 [econ.GN], CC BY 4.0; contains the abstract, Section 6 (Results, with Table 6 \"Overall Average Treatment Effect on the Treated (ATT)\" by buffer distance and Table 7 \"Overall ATT by Outcome Category and Buffer Zone\") and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants; ATTs are in log points; the line below each coefficient row holds standard errors in parentheses; stars denote p < 0.01 (***), p < 0.05 (**), p < 0.10 (*).\n- `data/sources/arxiv-2606.14621v1.txt` — extract of Li, Pearson-Merkowitz & Yokum (2026), *Evaluating the Impact of Rhode Island's Self-Sustaining RESEA Program on Employment Outcomes*, arXiv:2606.14621v1 [econ.GN], CC BY 4.0; contains the abstract, Section 3 (Results, with Table 2: group means, treatment-control differences and average treatment effect (ATE) estimates with two-sided p-values and 95% confidence intervals) and Table 3 (ATE estimates under three estimators). Wages are annualised US dollars; reemployment is in percent / percentage points (pp); weeks on UI are weeks. Tables 2 and 3 were re-set in a one-row-per-outcome layout (see the extract header); values are verbatim.\n- `data/README.md` — provenance, retrieval times, licences and SHA-256 of the extracts.\n\n## Information-availability rules\n- The two extracts are the only admissible evidence; do not use remembered values from the papers or other versions.\n- \"As printed\" means the sign and the number of decimals shown in the table that the key names (e.g. `-0.431`, `0.210`, `17250` for \"17,250\"). Where a value appears in more than one table, use the table named in the key's definition.\n- Derived keys must be computed from the printed values of the named table with a stated convention (normal 1.96 or 2.0, or a t critical value); the convention must be written in `derivation_notes`.\n- A locator has the form `<source id> | Table <n> | row \"<row label>\" | column \"<column label>\"`; for derived keys, name the table the inputs came from and say \"derived\".\n- If a requested quantity is not printed in the source (and cannot be derived from printed quantities without additional assumptions), report `null` and say so in the locator (e.g. `not reported in arxiv-2609.27732v1`).\n\n## Required outputs\n- `out/results.json` with the keys (all numbers \"as printed\" unless marked derived):\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `dcr_att_5km` | number | log points | Table 6, row \"ATT\", column \"5 km\" |\n| `dcr_att_5km_se` | number | log points | Table 6, standard error of the 5 km ATT |\n| `dcr_att_3km` | number | log points | Table 6, row \"ATT\", column \"3 km\" |\n| `dcr_att_3km_se` | number | log points | Table 6, standard error of the 3 km ATT |\n| `dcr_obs_5km` | integer | region-years | Table 6, row \"Observations\", column \"5 km\" |\n| `dcr_narcotics_att_3km` | number | log points | Table 7, row \"Narcotics\", column \"3 km\" |\n| `dcr_narcotics_att_3km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_fatal_att_1km` | number | log points | Table 7, row \"Drug-related (fatal)\", column \"1 km\" |\n| `dcr_fatal_att_1km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km` | number | log points | Table 7, row \"HIV\", column \"5 km\" |\n| `dcr_hiv_att_5km_se` | number | log points | Table 7, standard error of that cell |\n| `dcr_hiv_att_5km_p_value` | number or null | — | the exact two-sided p-value of the HIV 5 km ATT if the source prints it; otherwise null |\n| `dcr_hiv_att_5km_significant_10pct` | boolean | — | whether the source marks the HIV 5 km ATT as significant at the 10% level |\n| `dcr_att_5km_ci_low`, `dcr_att_5km_ci_high` | number | log points | **derived**: 95% confidence interval of the Table 6 5 km ATT from its printed SE |\n| `ri_wage_ate` | number | USD per year | Table 2, row \"Wage\", column \"Average treatment effect estimate\", point estimate |\n| `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high` | number | USD per year | Table 2, the 95% CI bounds printed for that ATE |\n| `ri_wage_ate_se_reported` | boolean | — | whether the source prints a standard error for the wage ATE |\n| `ri_wage_ate_se_derived` | number | USD per year | **derived**: standard error implied by the printed 95% CI (state the critical value used) |\n| `ri_control_mean_wage` | number | USD per year | Table 2, row \"Wage\", column \"Control\" (mean) |\n| `ri_reemployment_ate_pp` | number | percentage points | Table 2, row \"Reemployment\", ATE point estimate |\n| `ri_weeks_ui_ate` | number | weeks | Table 2, row \"Weeks on UI\", ATE point estimate (sign as printed: negative = fewer weeks) |\n| `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high` | number | weeks | Table 2, the 95% CI bounds printed for that ATE (as printed, lower bound first) |\n| `locators` | object | — | one locator string per key above (except the two booleans) |\n| `derivation_notes` | object | — | {\"dcr_att_5km_ci\": \"<convention>\", \"ri_wage_ate_se\": \"<convention>\"} |\n| `task_id` | string | — | \"F4-conf-01\" |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" — refers to the task as a whole |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the inputs (rendering defects, internal inconsistencies of a source, etc.) and how they were handled; empty list if none |\n| `n_input_rows` | object | lines / items | {file name: number of lines} for every file under `data/` that you read (text files: number of newline-terminated lines as reported by `wc -l`; JSON files: number of top-level items) |\n| `n_analysed` | integer or null | — | number of reported items (values, assertions, effects or references) you actually assessed |\n| `random_seed` | integer or null | — | null (no randomness is involved) |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The Uncertainty section must say which reported uncertainties are standard errors and which are confidence intervals, and what the derived quantities assume.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically (text parsing is acceptable; hard-coding the numbers is acceptable only if the script also prints the matched source lines).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\nanalysis.py\nclaims.json\nexclusions.csv\nreport.md\nresults.json\nreview\nstate\n\nout/review:\n\nout/state:\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\n\nout/state/gate_receipts:\n20261030T052611.849624Z.json\n20261030T052657.688138Z.json\n20261030T052701.730727Z.json\n20261030T052712.843695Z.json\n{\n \"dcr_att_5km\": -0.431,\n \"dcr_att_5km_se\": 0.210,\n \"dcr_att_3km\": -0.283,\n \"dcr_att_3km_se\": 0.279,\n \"dcr_narcotics_att_3km\": 0.549,\n \"dcr_narcotics_att_3km_se\": 0.187,\n \"dcr_fatal_att_1km\": -1.207,\n \"dcr_fatal_att_1km_se\": 0.137,\n \"dcr_hiv_att_5km\": -0.340,\n \"dcr_hiv_att_5km_se\": 0.294,\n \"dcr_hiv_att_5km_significant_10pct\": false,\n \"dcr_obs_5km\": 17250,\n \"dcr_hiv_att_5km_p_value\": null,\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\": 503.8265,\n \"ri_control_mean_wage\": 33043,\n \"ri_reemployment_ate_pp\": 1.47,\n \"ri_weeks_ui_ate\": -1.99,\n \"ri_weeks_ui_ate_ci_low\": -2.24,\n \"ri_weeks_ui_ate_ci_high\": -1.75,\n \"locators\": {\n \"dcr_att_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n \"dcr_att_5km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_att_3km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\"\",\n \"dcr_att_3km_se\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"3 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_narcotics_att_3km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\"\",\n \"dcr_narcotics_att_3km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Narcotics\\\" | column \\\"3 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_fatal_att_1km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\"\",\n \"dcr_fatal_att_1km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Drug-related (fatal)\\\" | column \\\"1 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_hiv_att_5km\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_se\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"5 km\\\" | standard error in parentheses on the line below the coefficient\",\n \"dcr_obs_5km\": \"arxiv-2609.27732v1 | Table 6 | row \\\"Observations\\\" | column \\\"5 km\\\"\",\n \"dcr_hiv_att_5km_p_value\": \"not reported in arxiv-2609.27732v1 (Table 7 prints only the coefficient, SE and significance stars; no exact p-value is printed anywhere in the extract)\",\n \"dcr_att_5km_ci_low\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (and SE line below) | column \\\"5 km\\\" | derived: estimate -/+ 1.96 x SE\",\n \"dcr_att_5km_ci_high\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" (and SE line below) | column \\\"5 km\\\" | derived: estimate -/+ 1.96 x SE\",\n \"ri_wage_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | point estimate\",\n \"ri_wage_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI lower bound\",\n \"ri_wage_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI upper bound\",\n \"ri_wage_ate_se_derived\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Average treatment effect estimate\\\" | derived: (CI upper - CI lower) / (2 x 1.96); SE not reported in arxiv-2606.14621v1\",\n \"ri_control_mean_wage\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Wage\\\" | column \\\"Control\\\" | mean (SD in parentheses not used)\",\n \"ri_reemployment_ate_pp\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Reemployment\\\" | column \\\"Average treatment effect estimate\\\" | point estimate (pp)\",\n \"ri_weeks_ui_ate\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | point estimate\",\n \"ri_weeks_ui_ate_ci_low\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI lower bound\",\n \"ri_weeks_ui_ate_ci_high\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\" | 95% CI upper bound\"\n },\n \"derivation_notes\": {\n \"dcr_att_5km_ci\": \"95% CI = ATT -/+ 1.96 x SE (normal approximation, critical value 1.96) using the printed Table 6 values ATT=-0.431, SE=0.210; rounded to 4 decimals. Table 6 does not print a CI. Sensitivity with critical value 2.0: see baseline_dcr_att_5km_ci_low_z2 / _high_z2.\",\n \"ri_wage_ate_se\": \"SE = (upper - lower) / (2 x 1.96) using the printed Table 2 95% CI [166, 2,141] (normal approximation, critical value 1.96, assumes a symmetric Wald-type interval); rounded to 4 decimals. The source prints no SE.\"\n },\n \"task_id\": \"F4-conf-01\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Same 5 km CS-DR ATT (-0.431) has SE 0.210 in Table 6 but SE 0.218 in Table A19 of arxiv-2609.27732v1.\",\n \"handling\": \"Keys name Table 6, so the Table 6 SE (0.210) is used for dcr_att_5km_se and the derived CI; the discrepancy is reported, not resolved.\"\n },\n {\n \"issue\": \"Control-region count inconsistent in arxiv-2609.27732v1: Tables 6/7 and Fig. 6 note say 608 never-treated regions, Table A19 note says 640. Table 6 observations divided by (treated + 640) = [25.0, 25.0, 25.0]; by (treated + 608) = [26.301, 26.244, 26.216].\",\n \"handling\": \"dcr_obs_5km reported as printed (17250); the arithmetic suggests the panel uses 640 controls x 25 periods, but this is not asserted as fact.\"\n },\n {\n \"issue\": \"Narrative of arxiv-2609.27732v1 is inconsistent with itself: abstract says fatal hospitalisations fall by 35%, Section 6.1 says ~30% at 5 km (Table 7 5 km ATT -0.354 -> exp(-0.354)-1 = -0.298); text calls HIV estimates 'close to zero' although 5 km ATT is -0.340 (not significant).\",\n \"handling\": \"Only table values are extracted; narrative claims are not used.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1 narrative reports the weeks-on-UI CI as unsigned magnitudes [1.75, 2.24] while Table 2 prints [-2.24, -1.75].\",\n \"handling\": \"Signed Table 2 values used, lower bound first.\"\n },\n {\n \"issue\": \"Rendering defect in arxiv-2606.14621v1 extract line 39: 'p ¡ 0.001' (should read p < 0.001).\",\n \"handling\": \"No extracted key affected; ignored.\"\n },\n {\n \"issue\": \"arxiv-2606.14621v1: Table 2 labels the column 'Average treatment effect estimate' while the Table 3 caption calls the same numbers ITT estimates; Table 2 caption does not state the CI construction beyond 'methodology described in the main text' (omitted from the extract).\",\n \"handling\": \"Values taken from Table 2 as required; SE derivation assumes a symmetric normal-approximation interval (critical value 1.96), which is an assumption.\"\n }\n ],\n \"n_input_rows\": {\n \"README.md\": 10,\n \"arxiv-2609.27732v1.txt\": 134,\n \"arxiv-2606.14621v1.txt\": 58\n },\n \"n_analysed\": 25,\n \"random_seed\": null,\n \"baseline_dcr_att_5km_ci_low_z2\": -0.851,\n \"baseline_dcr_att_5km_ci_high_z2\": -0.011,\n \"baseline_ri_wage_ate_se_derived_z2\": 493.75,\n \"check_ri_wage_ci_midpoint\": 1153.5,\n \"check_ri_wage_p_implied_by_derived_se\": 0.0221,\n \"check_dcr_att_5km_p_implied_normal\": 0.0401,\n \"check_dcr_hiv_5km_abs_z\": 1.1565,\n \"check_dcr_a19_se_5km\": 0.218,\n \"check_table3_wage_identical_to_table2\": true\n}\n[\n {\"id\":\"C1\",\"text\":\"Table 6 of arxiv-2609.27732v1 prints a 5 km ATT of -0.431 (**) with SE 0.210 and 17250 observations; the 3 km ATT is -0.283 (SE 0.279).\",\"results_keys\":[\"dcr_att_5km\",\"dcr_att_5km_se\",\"dcr_obs_5km\",\"dcr_att_3km\",\"dcr_att_3km_se\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same estimate; Table 6 used as the key requires.\"},\n {\"id\":\"C2\",\"text\":\"Table 7 prints Narcotics 3 km ATT 0.549 (SE 0.187), Drug-related (fatal) 1 km ATT -1.207 (SE 0.137), and HIV 5 km ATT -0.340 (SE 0.294).\",\"results_keys\":[\"dcr_narcotics_att_3km\",\"dcr_narcotics_att_3km_se\",\"dcr_fatal_att_1km\",\"dcr_fatal_att_1km_se\",\"dcr_hiv_att_5km\",\"dcr_hiv_att_5km_se\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"The HIV 5 km ATT carries no significance star (not significant at 10%) and its exact p-value is not reported in the extract.\",\"results_keys\":[\"dcr_hiv_att_5km_significant_10pct\",\"dcr_hiv_att_5km_p_value\",\"check_dcr_hiv_5km_abs_z\"],\"status\":\"supported\",\"caveat\":\"p-value set to null rather than derived.\"},\n {\"id\":\"C4\",\"text\":\"Using ATT +/- 1.96 x SE, the derived 95% CI of the Table 6 5 km ATT is [-0.8426, -0.0194]; with critical value 2.0 it is [-0.851, -0.011].\",\"results_keys\":[\"dcr_att_5km_ci_low\",\"dcr_att_5km_ci_high\",\"baseline_dcr_att_5km_ci_low_z2\",\"baseline_dcr_att_5km_ci_high_z2\"],\"status\":\"supported\",\"caveat\":\"Derived under a normal approximation; not printed in the source.\"},\n {\"id\":\"C5\",\"text\":\"Table 2 of arxiv-2606.14621v1 prints a wage ATE of $1153 per year with 95% CI [166, 2141] and no standard error; control mean wage is $33043.\",\"results_keys\":[\"ri_wage_ate\",\"ri_wage_ate_ci_low\",\"ri_wage_ate_ci_high\",\"ri_wage_ate_se_reported\",\"ri_control_mean_wage\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"The SE implied by the printed wage CI with critical value 1.96 is 503.8265 (493.75 with 2.0), and it reproduces the printed p=0.022 (implied 0.0221).\",\"results_keys\":[\"ri_wage_ate_se_derived\",\"baseline_ri_wage_ate_se_derived_z2\",\"check_ri_wage_p_implied_by_derived_se\",\"check_ri_wage_ci_midpoint\"],\"status\":\"supported\",\"caveat\":\"Assumes a symmetric normal-approximation CI; CI construction not described in the extract; CI bounds are rounded.\"},\n {\"id\":\"C7\",\"text\":\"Table 2 prints a reemployment ATE of 1.47 pp and a weeks-on-UI ATE of -1.99 weeks with 95% CI [-2.24, -1.75].\",\"results_keys\":[\"ri_reemployment_ate_pp\",\"ri_weeks_ui_ate\",\"ri_weeks_ui_ate_ci_low\",\"ri_weeks_ui_ate_ci_high\"],\"status\":\"supported\",\"caveat\":\"Narrative reports the weeks CI as unsigned magnitudes; signed Table 2 values used.\"},\n {\"id\":\"C8\",\"text\":\"The DCR extract is internally inconsistent about the control-region count (608 vs 640) and the 5 km SE (0.210 vs 0.218); observation counts equal 25 x (treated + 640).\",\"results_keys\":[\"data_issues\",\"check_dcr_a19_se_5km\",\"dcr_obs_5km\"],\"status\":\"supported\",\"caveat\":\"The arithmetic is suggestive; which count is correct is not resolved by the extract.\"}\n]\n# F4-conf-01 — Extracting treatment effects and their uncertainty from two published tables\n\n## Question\nThe task is to pull specified treatment effects, standard errors (SEs), 95% confidence-interval (CI) bounds and counts out of two arXiv extracts, copying them exactly as printed. The sources are Armendariz (2026), arXiv:2609.27732v1, a staggered DiD study of Swiss drug consumption rooms (DCRs), using Tables 6 and 7; and Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, an RCT of Rhode Island's RESEA programme, using Table 2. Each value gets a locator. I also derive two quantities the sources do not print, state the convention used for each, and flag requested quantities that are not reported. Nothing is re-estimated.\n\n## Data and exclusions\n- Inputs: `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) and `data/README.md` (10 lines) (`n_input_rows`). The SHA-256 of each extract matches the one listed in the README (`out/state/data_audit.md`).\n- Nothing was filtered out. All 25 requested value items were found and assessed (`n_analysed` = 25; `out/exclusions.csv` shows 0 removed).\n- Information rules: only the two extracts were used. Two other tables, DCR Table A19 and RESEA Table 3, also print some of the same numbers. I used them only for cross-checks, never as the source of a key.\n- Problems found in the inputs (`data_issues`, 6 entries):\n 1. Table 6 gives the 5 km CS-DR ATT (-0.431**) an SE of 0.210. Table A19 prints the same estimate with SE 0.218 (`check_dcr_a19_se_5km`). The key names Table 6, so I used Table 6.\n 2. The number of control regions is inconsistent: 608 in Tables 6 and 7, but 640 in the Table A19 note. The printed observation counts are exactly 25 × (treated + 640) and are not whole multiples of (treated + 608). I report the observation count as printed.\n 3. The DCR narrative contradicts itself. The abstract says fatal hospitalisations fall by 35%, while Section 6.1 says about 30% at 5 km. The text also calls the HIV estimates \"close to zero\", although the 5 km estimate is -0.340.\n 4. The RESEA narrative gives the weeks-on-UI CI as unsigned magnitudes. I used the signed values from Table 2.\n 5. There is a rendering defect, \"p ¡ 0.001\", in the RESEA text.\n 6. The RESEA tables label the same numbers differently: \"ATE\" in Table 2 and ITT in Table 3. The extract does not describe how the CIs were built.\n\n## Method\n`out/analysis.py` parses the \" | \"-separated table lines and attaches each SE line in parentheses to the coefficient row above it. It removes `$`, thousands separators, stars and units, keeps the sign, and writes each printed literal into the JSON so that trailing zeros survive (for example 0.210 and -0.340). It also prints every source line it matched. Significance at the 10% level is read from the stars: any of `*`, `**` or `***` means p < 0.10.\n\nThe two derived quantities use the normal critical value 1.96 (`derivation_notes`):\n- the DCR 95% CI is ATT ± 1.96 × SE;\n- the RESEA wage SE is (upper − lower)/(2 × 1.96).\n\nAs a sensitivity check I repeated both with a critical value of 2.0 (`baseline_*` keys). The script involves no randomness (`random_seed` = null).\n\n## Results\n**DCR study (arxiv-2609.27732v1), log points**\n\n| Quantity | Estimate | SE | Keys |\n|---|---|---|---|\n| Table 6 ATT, 5 km | -0.431 (\\*\\*) | 0.210 | `dcr_att_5km`, `dcr_att_5km_se` |\n| Table 6 ATT, 3 km | -0.283 | 0.279 | `dcr_att_3km`, `dcr_att_3km_se` |\n| Table 7 Narcotics, 3 km | 0.549 (\\*\\*\\*) | 0.187 | `dcr_narcotics_att_3km`, `dcr_narcotics_att_3km_se` |\n| Table 7 Drug-related (fatal), 1 km | -1.207 (\\*\\*\\*) | 0.137 | `dcr_fatal_att_1km`, `dcr_fatal_att_1km_se` |\n| Table 7 HIV, 5 km | -0.340 (no star) | 0.294 | `dcr_hiv_att_5km`, `dcr_hiv_att_5km_se` |\n\n- Table 6 reports 17250 observations for the 5 km buffer (`dcr_obs_5km`).\n- The source does not mark the HIV 5 km ATT as significant at the 10% level (`dcr_hiv_att_5km_significant_10pct` = false).\n- Its exact p-value is not printed, so it is reported as null (`dcr_hiv_att_5km_p_value`). The tables give significance stars only.\n- Derived 95% CI for the 5 km ATT: [-0.8426, -0.0194] (`dcr_att_5km_ci_low`, `dcr_att_5km_ci_high`).\n\n**RESEA study (arxiv-2606.14621v1), Table 2**\n- Wage ATE: $1153 per year, 95% CI [166, 2141] (`ri_wage_ate`, `ri_wage_ate_ci_low`, `ri_wage_ate_ci_high`).\n- No SE is printed for the wage ATE (`ri_wage_ate_se_reported` = false). The SE implied by the CI is 503.8265 (`ri_wage_ate_se_derived`).\n- Control-group mean wage: $33043 (`ri_control_mean_wage`).\n- Reemployment ATE: 1.47 pp (`ri_reemployment_ate_pp`).\n- Weeks-on-UI ATE: -1.99 weeks, 95% CI [-2.24, -1.75] (`ri_weeks_ui_ate`, `ri_weeks_ui_ate_ci_low`, `ri_weeks_ui_ate_ci_high`).\n\nEvery locator is in `locators`. Feasibility is \"complete\" (`feasibility`).\n\n## Uncertainty\n- **DCR values are standard errors.** In Tables 6 and 7 the numbers in parentheses are SEs clustered at the MedStat region level. The stars mark p < 0.01, 0.05 and 0.10.\n- **RESEA values are confidence intervals and p-values.** Table 2 prints two-sided p-values and 95% CIs, and no SEs.\n- **The derived DCR CI assumes normality.** It assumes the ATT is approximately normally distributed with the printed SE, and uses a critical value of 1.96. It is consistent with the source: it excludes 0, and the implied normal p is 0.0401 (`check_dcr_att_5km_p_implied_normal`), which fits the printed `**`. With a critical value of 2.0 the CI would be [-0.851, -0.011] (`baseline_dcr_att_5km_ci_low_z2`, `baseline_dcr_att_5km_ci_high_z2`).\n- **The derived wage SE assumes a symmetric normal-approximation CI.** It assumes the printed CI is estimate ± 1.96 × SE. Two checks support this:\n - the CI midpoint is 1153.5 (`check_ri_wage_ci_midpoint`);\n - the p-value implied by the derived SE is 0.0221 (`check_ri_wage_p_implied_by_derived_se`), close to the printed p = 0.022.\n\n With a critical value of 2.0 the SE would be 493.75 (`baseline_ri_wage_ate_se_derived_z2`). The extract does not describe how the CI was actually built. Because the CI bounds are rounded to whole dollars, the derived SE is only accurate to about ±0.3.\n- **The HIV 5 km estimate is not significant.** Its |ATT/SE| is 1.1565 (`check_dcr_hiv_5km_abs_z`), below 1.645, which matches the absence of a star.\n\n## Limitations and open issues\n- **The SE of the 5 km ATT is ambiguous.** Tables 6 and A19 print different SEs (0.210 vs 0.218) for the same estimate. I followed the key's definition and used Table 6, but the source itself is inconsistent. With 0.218 the derived CI would be wider.\n- **The number of control regions is uncertain.** The count is printed as 608, but 640 fits the observation counts. Which one is right is unresolved in the extract.\n- **The exact HIV p-value cannot be given.** It is not reported, so it is null. Recovering it from the SE would require a distributional assumption, and the key asks only for a printed value.\n- **The derived quantities rest on convention.** Both use the normal 1.96 critical value. A t critical value could not be used because the degrees of freedom are not reported (for DCR, the clusters are not stated consistently).\n- **The 3.5% wage figure was a check, not an extracted value.** The ratio of the wage ATE to the control mean matches the narrative's 3.5% (`out/state/checks.md`, check 15).","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2504accd-ebc8-4110-ab81-1a2300d403bf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","hash":"264f3abb75bd500d83d2606ceb71d50bb5b8ada88138d934d7c0dd1e546b1507","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01BqyXnfZZeZWcc3rg8TkiNv","name":"Bash","input":{"command":"cat TASK.md; ls -R out; cat out/results.json; cat out/claims.json; cat out/report.md"}}]}}15{"type":"api-request","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","id":"f36c8e52-4b7f-4dee-9cad-f41ff4f9ba7c","timestamp":"2026-10-30T05:27:15.192Z","version":"2.1.314","querySource":"sdk","shapeHash":"e75fa6184f54c325da01a326d15f9b377bc3c835f3f564e705d266156d0b4525","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":"0df11312-5b0b-46d4-8f35-25a5cd62240c","keep":2,"tail":["264f3abb75bd500d83d2606ceb71d50bb5b8ada88138d934d7c0dd1e546b1507","26f4e12fc5fe43c10b35f5083de3f769842a8cb05bc124840816f9c8c0bff73f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8593f35c3371d7590d9552791a8ceb440f6353a11401c3dd96075222b0f64ccb"}16{"type":"last-prompt","lastPrompt":"Review","leafUuid":"9704899b-38c4-4a6a-85d0-d8a95c22e8c9","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}17{"type":"atis-latch","atis":"","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}18{"parentUuid":"9704899b-38c4-4a6a-85d0-d8a95c22e8c9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsJ6gK132W4kSy1nz9Z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JHoFXk7Jt7tCaqYZGUJYer","name":"Bash","input":{"command":"cat data/sources/*.txt; cat data/README.md; wc -l data/README.md data/sources/*; cat out/analysis.py; cat out/exclusions.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11840,"cache_read_input_tokens":2220,"output_tokens":326,"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":11840},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsJ6QT5zrkztJfTg2YX","requestRef":"f36c8e52-4b7f-4dee-9cad-f41ff4f9ba7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"24995eb1-5ed8-40cd-ba83-f14335bd5484","timestamp":"2026-10-30T05:27:17.555Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}19{"parentUuid":"24995eb1-5ed8-40cd-ba83-f14335bd5484","isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JHoFXk7Jt7tCaqYZGUJYer","type":"tool_result","content":"<persisted-output>\nOutput too large (40.1KB). Full output saved to: /Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-wp29-runs-RC0283/6ab152be-b2cd-455c-9124-d3f247276d7c/tool-results/b8yawjilk.txt\n\nPreview (first 2KB):\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n...\n</persisted-output>","is_error":false}]},"uuid":"b5c31a34-267e-462b-8e30-67328cd9384e","timestamp":"2026-10-30T05:27:17.578Z","toolUseResult":{"stdout":"SOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged 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\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[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\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $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]\nReemployment | 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]\nWeeks 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]\nShort-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]\nShort-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]\nShort-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\nTable 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\nOverall, 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\nThe 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\nAs 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\n### 3.1 Cost-benefit analysis\n\nWe 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\n### 4.1 Methodological lessons (excerpt)\n\n[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\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $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]\nReemployment | 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]\nWeeks 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\nTable 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\nIf 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\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic 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\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering 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================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis 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\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis 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\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: 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\nFigure 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\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: 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\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 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\ncorresponding 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\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-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\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: 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\nDCR 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\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 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\n### 6.1 Mechanisms\n\nThe 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\nsubstitution 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(Kerr et al., 2017; Hedrich, 2004).\n\nThe 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\nA 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\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\n# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n 10 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 202 total\n\"\"\"F4-conf-01: extract printed values from two arXiv extracts and derive two quantities.\nReads only data/; writes out/results.json deterministically. No randomness (random_seed = null).\nPrints every matched source line so values can be traced.\"\"\"\nimport json, os, re, math\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDCR_ID, RI_ID = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\nDCR = os.path.join(ROOT, \"data\", \"sources\", DCR_ID + \".txt\")\nRI = os.path.join(ROOT, \"data\", \"sources\", RI_ID + \".txt\")\nREADME = os.path.join(ROOT, \"data\", \"README.md\")\nZ = 1.96 # normal critical value for two-sided 95% intervals\n\ndef lines_of(p):\n with open(p, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(p):\n with open(p, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\ndef lit(s):\n \"\"\"Printed literal with $, commas, stars, units removed (keeps trailing zeros).\"\"\"\n s = s.strip().replace(\"$\", \"\").replace(\",\", \"\")\n s = re.sub(r\"\\*+\", \"\", s)\n return re.search(r\"-?\\d+(?:\\.\\d+)?\", s).group(0)\n\ndef num(s):\n \"\"\"Parse a printed number: strip $, thousands commas, stars, %, units. Keep sign.\"\"\"\n s = s.strip().replace(\"$\", \"\").replace(\",\", \"\")\n s = re.sub(r\"\\*+\", \"\", s)\n m = re.search(r\"-?\\d+(?:\\.\\d+)?\", s)\n v = m.group(0)\n return int(v) if re.fullmatch(r\"-?\\d+\", v) else float(v)\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\ndef find_table(lines, title_prefix):\n for i, l in enumerate(lines):\n if l.startswith(title_prefix):\n return i\n raise ValueError(title_prefix)\n\ndef dcr_table(lines, title_prefix):\n \"\"\"Return header columns and dict row_label -> (coef cells, se cells or None, line numbers).\"\"\"\n i = find_table(lines, title_prefix)\n j = i + 1\n while not lines[j].strip():\n j += 1\n header = cells(lines[j])[1:] # first cell is row-label column ('' or 'Outcome')\n rows, k, nonblank = {}, j + 1, []\n while k < len(lines) and not lines[k].startswith(\"Note:\"):\n if lines[k].strip():\n nonblank.append(k)\n k += 1\n for idx, ln in enumerate(nonblank):\n c = cells(lines[ln])\n if c[0] == \"\":\n continue # SE line, attached below\n se, se_ln = None,","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false,"persistedOutputPath":"/Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-wp29-runs-RC0283/6ab152be-b2cd-455c-9124-d3f247276d7c/tool-results/b8yawjilk.txt","persistedOutputSize":41033},"sandboxed":false,"sourceToolAssistantUUID":"24995eb1-5ed8-40cd-ba83-f14335bd5484","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"b5c31a34-267e-462b-8e30-67328cd9384e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsJ6gK132W4kSy1nz9Z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011UJvmQoY6JSUG4amKHuV1m","name":"Bash","input":{"command":"rm -rf /tmp/rc283 && mkdir /tmp/rc283 && cp -r data out /tmp/rc283/ && cd /tmp/rc283 && ~/wp29/venv/bin/python out/analysis.py >/tmp/rc283/log.txt 2>&1; echo $?; tail -5 log.txt; diff <(python3 -m json.tool /tmp/rc283/out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0283/out/results.json) && echo SAME"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":11840,"cache_read_input_tokens":2220,"output_tokens":326,"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":11840},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsJ6QT5zrkztJfTg2YX","requestRef":"f36c8e52-4b7f-4dee-9cad-f41ff4f9ba7c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b8ad5273-7318-439c-be70-f318fc3d063c","timestamp":"2026-10-30T05:27:18.828Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"b8ad5273-7318-439c-be70-f318fc3d063c","isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011UJvmQoY6JSUG4amKHuV1m","type":"tool_result","content":"0\n matched line 29: row 'Reemployment' col 'Average treatment effect estimate' -> '1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'\n matched line 30: row 'Weeks on UI' col 'Average treatment effect estimate' -> '-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]'\n matched line 123: row 'ATT' col 'CS-DR' -> '(0.218)'\n matched line 121: row 'ATT' col 'CS-DR' -> '-0.431**'\nWrote out/results.json with 25 value keys\nSAME\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0283","is_error":false}]},"uuid":"90a70fd1-a937-47b2-a7e5-29e5578e362f","timestamp":"2026-10-30T05:27:18.912Z","toolUseResult":{"stdout":"0\n matched line 29: row 'Reemployment' col 'Average treatment effect estimate' -> '1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'\n matched line 30: row 'Weeks on UI' col 'Average treatment effect estimate' -> '-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]'\n matched line 123: row 'ATT' col 'CS-DR' -> '(0.218)'\n matched line 121: row 'ATT' col 'CS-DR' -> '-0.431**'\nWrote out/results.json with 25 value keys\nSAME","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0283","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b8ad5273-7318-439c-be70-f318fc3d063c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}22{"type":"api-request-blob","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","hash":"83f447a010fed9c8f3bc49a651ba05ee1f1c6a0e06f03cfee71f782f20b3e0c1","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01JHoFXk7Jt7tCaqYZGUJYer","name":"Bash","input":{"command":"cat data/sources/*.txt; cat data/README.md; wc -l data/README.md data/sources/*; cat out/analysis.py; cat out/exclusions.csv"}},{"type":"tool_use","id":"toolu_011UJvmQoY6JSUG4amKHuV1m","name":"Bash","input":{"command":"rm -rf /tmp/rc283 && mkdir /tmp/rc283 && cp -r data out /tmp/rc283/ && cd /tmp/rc283 && ~/wp29/venv/bin/python out/analysis.py >/tmp/rc283/log.txt 2>&1; echo $?; tail -5 log.txt; diff <(python3 -m json.tool /tmp/rc283/out/results.json) <(python3 -m json.tool ~/wp29/runs/RC0283/out/results.json) && echo SAME"}}]}}23{"type":"api-request-blob","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","hash":"8ba7e390b07ddf7bb79082374df806092aedb7e3213390f4ddc24f584311ea84","message":{"role":"user","content":[{"tool_use_id":"toolu_01JHoFXk7Jt7tCaqYZGUJYer","type":"tool_result","content":"<persisted-output>\nOutput too large (40.1KB). Full output saved to: /Users/simon-pierreboucher/.claude/projects/-Users-simon-pierreboucher-wp29-runs-RC0283/6ab152be-b2cd-455c-9124-d3f247276d7c/tool-results/b8yawjilk.txt\n\nPreview (first 2KB):\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic 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\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract 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.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering 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.\nEditorial 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================================================================\n\n...\n</persisted-output>","is_error":false},{"tool_use_id":"toolu_011UJvmQoY6JSUG4amKHuV1m","type":"tool_result","content":"0\n matched line 29: row 'Reemployment' col 'Average treatment effect estimate' -> '1.47 pp; p=0.013; 95% CI: [0.32, 2.62]'\n matched line 30: row 'Weeks on UI' col 'Average treatment effect estimate' -> '-1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]'\n matched line 123: row 'ATT' col 'CS-DR' -> '(0.218)'\n matched line 121: row 'ATT' col 'CS-DR' -> '-0.431**'\nWrote out/results.json with 25 value keys\nSAME\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0283","is_error":false}]}}24{"type":"api-request","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","id":"3b075bbf-5b8f-4a19-91c0-58f4f05f4da9","timestamp":"2026-10-30T05:27:18.914Z","version":"2.1.314","querySource":"sdk","shapeHash":"e75fa6184f54c325da01a326d15f9b377bc3c835f3f564e705d266156d0b4525","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":"f36c8e52-4b7f-4dee-9cad-f41ff4f9ba7c","keep":4,"tail":["83f447a010fed9c8f3bc49a651ba05ee1f1c6a0e06f03cfee71f782f20b3e0c1","8ba7e390b07ddf7bb79082374df806092aedb7e3213390f4ddc24f584311ea84"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"de9aba2a6668120a72941b327b3601360a9fd93a554a100ab99f90c29a0c2fd2"}25{"type":"last-prompt","lastPrompt":"Review","leafUuid":"90a70fd1-a937-47b2-a7e5-29e5578e362f","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}26{"type":"atis-latch","atis":"","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}27{"parentUuid":"90a70fd1-a937-47b2-a7e5-29e5578e362f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsJNZJ7jUpJYKRdeEmH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V6EDsTdnHBPbSwZnBCUNnM","name":"Bash","input":{"command":"cd data/sources; grep -n \"|\" *.txt | head -80; grep -n -i \"p-value\\|608\\|640\\|35%\\|30%\" *.txt"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1521,"cache_read_input_tokens":14060,"output_tokens":98,"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":1521},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsJNJRKdRZoBpZeUyKR","requestRef":"3b075bbf-5b8f-4a19-91c0-58f4f05f4da9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b2694578-16a1-43b2-90ac-035430fbddc5","timestamp":"2026-10-30T05:27:20.770Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"b2694578-16a1-43b2-90ac-035430fbddc5","isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V6EDsTdnHBPbSwZnBCUNnM","type":"tool_result","content":"arxiv-2606.14621v1.txt:9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\narxiv-2606.14621v1.txt:25:[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]\narxiv-2606.14621v1.txt:27:Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt:31:Short-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]\narxiv-2606.14621v1.txt:32:Short-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]\narxiv-2606.14621v1.txt:33:Short-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]\narxiv-2606.14621v1.txt:49:[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]\narxiv-2606.14621v1.txt:51:Outcome | Difference in means | Covariate adjusted | Fixed effects regression\narxiv-2606.14621v1.txt:52:Wage | $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]\narxiv-2606.14621v1.txt:53:Reemployment | 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]\narxiv-2606.14621v1.txt:54:Weeks 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]\narxiv-2609.27732v1.txt:9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\narxiv-2609.27732v1.txt:34:| 1 km | 3 km | 5 km\narxiv-2609.27732v1.txt:36:ATT | -0.399 | -0.283 | -0.431**\narxiv-2609.27732v1.txt:38:| (0.412) | (0.279) | (0.210)\narxiv-2609.27732v1.txt:40:% change | -32.9% | -24.7% | -35.0%\narxiv-2609.27732v1.txt:42:Treated regions | 7 | 35 | 50\narxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\narxiv-2609.27732v1.txt:46:Observations | 16,175 | 16,875 | 17,250\narxiv-2609.27732v1.txt:68:Outcome | 1 km | 3 km | 5 km\narxiv-2609.27732v1.txt:70:Mental/Behavioral | -0.354 | -0.202 | -0.381*\narxiv-2609.27732v1.txt:72:| (0.382) | (0.244) | (0.220)\narxiv-2609.27732v1.txt:74:Hepatitis | -0.270 | -0.413 | -0.406\narxiv-2609.27732v1.txt:76:| (0.486) | (0.270) | (0.250)\narxiv-2609.27732v1.txt:78:HIV | 0.063 | -0.239 | -0.340\narxiv-2609.27732v1.txt:80:| (0.801) | (0.337) | (0.294)\narxiv-2609.27732v1.txt:82:Narcotics | 1.186*** | 0.549*** | 0.390**\narxiv-2609.27732v1.txt:84:| (0.288) | (0.187) | (0.165)\narxiv-2609.27732v1.txt:86:Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\narxiv-2609.27732v1.txt:88:| (0.137) | (0.215) | (0.178)\narxiv-2609.27732v1.txt:90:Treated regions | 7 | 35 | 50\narxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\narxiv-2609.27732v1.txt:119:| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\narxiv-2609.27732v1.txt:121:ATT | -0.431** | -0.438** | -0.362* | -0.149***\narxiv-2609.27732v1.txt:123:| (0.218) | (0.208) | (0.205) | (0.038)\narxiv-2609.27732v1.txt:125:% change | -35.0% | -35.5% | -30.3% | -13.9%\narxiv-2609.27732v1.txt:127:Treated regions | 50 | | |\narxiv-2609.27732v1.txt:129:Control regions | 608 | | |\narxiv-2609.27732v1.txt:131:Observations | 17,250 | | |\narxiv-2606.14621v1.txt:10:Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\narxiv-2609.27732v1.txt:18:This 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.\narxiv-2609.27732v1.txt:28:Note: 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.\narxiv-2609.27732v1.txt:30:Figure 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.\narxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\narxiv-2609.27732v1.txt:48:Note: 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\narxiv-2609.27732v1.txt:55:corresponding 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.\narxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\narxiv-2609.27732v1.txt:109:The 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\narxiv-2609.27732v1.txt:129:Control regions | 608 | | |\narxiv-2609.27732v1.txt:134:never-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"2860d40e-f60d-4380-9535-5f638a8f4d89","timestamp":"2026-10-30T05:27:20.786Z","toolUseResult":{"stdout":"arxiv-2606.14621v1.txt:9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\narxiv-2606.14621v1.txt:25:[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]\narxiv-2606.14621v1.txt:27:Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt: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]\narxiv-2606.14621v1.txt:31:Short-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]\narxiv-2606.14621v1.txt:32:Short-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]\narxiv-2606.14621v1.txt:33:Short-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]\narxiv-2606.14621v1.txt:49:[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]\narxiv-2606.14621v1.txt:51:Outcome | Difference in means | Covariate adjusted | Fixed effects regression\narxiv-2606.14621v1.txt:52:Wage | $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]\narxiv-2606.14621v1.txt:53:Reemployment | 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]\narxiv-2606.14621v1.txt:54:Weeks 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]\narxiv-2609.27732v1.txt:9:Rendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\narxiv-2609.27732v1.txt:34:| 1 km | 3 km | 5 km\narxiv-2609.27732v1.txt:36:ATT | -0.399 | -0.283 | -0.431**\narxiv-2609.27732v1.txt:38:| (0.412) | (0.279) | (0.210)\narxiv-2609.27732v1.txt:40:% change | -32.9% | -24.7% | -35.0%\narxiv-2609.27732v1.txt:42:Treated regions | 7 | 35 | 50\narxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\narxiv-2609.27732v1.txt:46:Observations | 16,175 | 16,875 | 17,250\narxiv-2609.27732v1.txt:68:Outcome | 1 km | 3 km | 5 km\narxiv-2609.27732v1.txt:70:Mental/Behavioral | -0.354 | -0.202 | -0.381*\narxiv-2609.27732v1.txt:72:| (0.382) | (0.244) | (0.220)\narxiv-2609.27732v1.txt:74:Hepatitis | -0.270 | -0.413 | -0.406\narxiv-2609.27732v1.txt:76:| (0.486) | (0.270) | (0.250)\narxiv-2609.27732v1.txt:78:HIV | 0.063 | -0.239 | -0.340\narxiv-2609.27732v1.txt:80:| (0.801) | (0.337) | (0.294)\narxiv-2609.27732v1.txt:82:Narcotics | 1.186*** | 0.549*** | 0.390**\narxiv-2609.27732v1.txt:84:| (0.288) | (0.187) | (0.165)\narxiv-2609.27732v1.txt:86:Drug-related (fatal) | -1.207*** | -0.290 | -0.354**\narxiv-2609.27732v1.txt:88:| (0.137) | (0.215) | (0.178)\narxiv-2609.27732v1.txt:90:Treated regions | 7 | 35 | 50\narxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\narxiv-2609.27732v1.txt:119:| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\narxiv-2609.27732v1.txt:121:ATT | -0.431** | -0.438** | -0.362* | -0.149***\narxiv-2609.27732v1.txt:123:| (0.218) | (0.208) | (0.205) | (0.038)\narxiv-2609.27732v1.txt:125:% change | -35.0% | -35.5% | -30.3% | -13.9%\narxiv-2609.27732v1.txt:127:Treated regions | 50 | | |\narxiv-2609.27732v1.txt:129:Control regions | 608 | | |\narxiv-2609.27732v1.txt:131:Observations | 17,250 | | |\narxiv-2606.14621v1.txt:10:Editorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\narxiv-2609.27732v1.txt:18:This 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.\narxiv-2609.27732v1.txt:28:Note: 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.\narxiv-2609.27732v1.txt:30:Figure 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.\narxiv-2609.27732v1.txt:44:Control regions | 608 | 608 | 608\narxiv-2609.27732v1.txt:48:Note: 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\narxiv-2609.27732v1.txt:55:corresponding 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.\narxiv-2609.27732v1.txt:92:Control regions | 608 (never-treated) | |\narxiv-2609.27732v1.txt:109:The 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. 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grep -n -i \"close to zero\\|HIV\" arxiv-2609.27732v1.txt | cut -c1-400; sed -n 1,10p ../README.md; grep -n \"1.96\\|t crit\\|cluster\" arxiv-2609.27732v1.txt | cut -c1-200; grep -n \"3.5\" arxiv-2606.14621v1.txt | cut -c1-300"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4469,"cache_read_input_tokens":15581,"output_tokens":412,"output_tokens_details":{"thinking_tokens":227},"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":4469},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsJWK7r2RtT782enaeX","requestRef":"ff85243f-6ffe-47a8-9d69-05030fd17f86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"489fa7f5-d5b4-46fd-a52c-df493ac2c515","timestamp":"2026-10-30T05:27:25.486Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283/data/sources","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"489fa7f5-d5b4-46fd-a52c-df493ac2c515","isSidechain":false,"promptId":"3f2f1c62-32f9-4a8c-b954-04929e9ca72a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EtH8BQQVddW64gjhYeByWZ","type":"tool_result","content":"(eval):cd:1: no such file or directory: data/sources\n18:This 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. T\n58:sub-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.\n78:HIV | 0.063 | -0.239 | -0.340\n99:HIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n105:Figure 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 Fig\n113:The 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 consiste\n# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n28:Note: 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-tr\n50:(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n64:DCR 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 o\n102:1998 excluded. Standard errors clustered at the Medstat region level.\n5:Retrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n19:Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (\n37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 i\n41:As noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly","is_error":false}]},"uuid":"e06bdf7d-75d6-4eeb-830e-04370ce893a9","timestamp":"2026-10-30T05:27:25.509Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data/sources\n18:This 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. T\n58:sub-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.\n78:HIV | 0.063 | -0.239 | -0.340\n99:HIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n105:Figure 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 Fig\n113:The 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 consiste\n# Data provenance — F4-conf-01\n\nAll files under `data/sources/` are plain-text extracts of publicly accessible documents, prepared by the task designer on 2026-10-30 (UTC). Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n28:Note: 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-tr\n50:(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n64:DCR 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 o\n102:1998 excluded. Standard errors clustered at the Medstat region level.\n5:Retrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n19:Prolonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (\n37:Overall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. 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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 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.\n73:h6, t6 = dcr_table(dl, \"Table 6:\")\n74:h7, t7 = dcr_table(dl, \"Table 7:\")\n89:print(\"DCR Table 6 / Table 7 extraction:\")\n106:# exact p-value of HIV 5 km: search Table 7 block and whole extract for a printed p-value\n109:R[\"dcr_hiv_att_5km_p_value\"] = None # Table 7 prints stars only\n110:L[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE and \"\n114:# derived 95% CI from Table 6 SE\n119: L[k] = f'{DCR_ID} | Table 6 | row \"ATT\" (and SE line below) | column \"5 km\" | derived: estimate -/+ 1.96 x SE'\n175: {\"issue\": f\"Same 5 km CS-DR ATT ({a19_b}) has SE {t6[\"ATT\"][1][h6.index(\"5 km\")].strip(\"()\")} in Table 6 but SE {tA[\"ATT\"][1][hA.index(\"CS-DR\")].strip(\"()\")} in Table A19 of {DCR_ID}.\",\n176: \"handling\": \"Keys name Table 6, so the Table 6 SE (0.210) is used for dcr_att_5km_se and the derived CI; the discrepancy is reported, not resolved.\"},","is_error":false}]},"uuid":"4134d56e-ce29-42ee-826c-d1694cb3a41d","timestamp":"2026-10-30T05:27:29.411Z","toolUseResult":{"stdout":"The 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 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.\n73:h6, t6 = dcr_table(dl, \"Table 6:\")\n74:h7, t7 = dcr_table(dl, \"Table 7:\")\n89:print(\"DCR Table 6 / Table 7 extraction:\")\n106:# exact p-value of HIV 5 km: search Table 7 block and whole extract for a printed p-value\n109:R[\"dcr_hiv_att_5km_p_value\"] = None # Table 7 prints stars only\n110:L[\"dcr_hiv_att_5km_p_value\"] = (f\"not reported in {DCR_ID} (Table 7 prints only the coefficient, SE and \"\n114:# derived 95% CI from Table 6 SE\n119: L[k] = f'{DCR_ID} | Table 6 | row \"ATT\" (and SE line below) | column \"5 km\" | derived: estimate -/+ 1.96 x SE'\n175: {\"issue\": f\"Same 5 km CS-DR ATT ({a19_b}) has SE {t6[\"ATT\"][1][h6.index(\"5 km\")].strip(\"()\")} in Table 6 but SE {tA[\"ATT\"][1][hA.index(\"CS-DR\")].strip(\"()\")} in Table A19 of {DCR_ID}.\",\n176: \"handling\": \"Keys name Table 6, so the Table 6 SE 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They are labelled clearly and harmless, but they are outside the specified schema.\",\n \"suggested_fix\": \"Optionally move them to a nested 'diagnostics' object so the top-level keys match the required schema exactly.\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"results.json:data_issues[1]; claims C8\",\n \"issue\": \"Table A19 itself prints 'Control regions | 608'. Only its note (line 134) says 640. The data issue could say this more precisely: the A19 table body and its note disagree, in addition to the disagreement between tables.\",\n \"suggested_fix\": \"Mention that A19's table body says 608 and its note says 640.\"\n }\n ],\n \"summary\": \"I re-ran analysis.py in a scratch copy. It is deterministic and reproduces results.json exactly, and it prints the source lines it matched. All required keys are present. I checked each value against the extracts: they are as printed (signs, decimals and thousands separators are handled correctly) and come from the tables the keys name (Table 6 SE 0.210, not A19's 0.218; Table 2, not Table 3). The derived CI [-0.8426, -0.0194] and the derived SE 503.8265 use 1.96 as stated, with 2.0 given as a sensitivity check. The HIV p-value is correctly null and the 10% flag is correctly false. The line counts match wc -l. The data issues are real and handled sensibly, and the claims match the numbers and carry appropriate caveats. Only minor wording and schema points remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}50{"type":"api-request","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","id":"d7a4c8ff-74b4-4ff3-993c-c577f4838820","timestamp":"2026-10-30T05:27:42.839Z","version":"2.1.314","querySource":"sdk","shapeHash":"e75fa6184f54c325da01a326d15f9b377bc3c835f3f564e705d266156d0b4525","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":"875af397-dc2e-42dc-8155-46fc4d89165a","keep":14,"tail":["248acbcba20ac4a89f4582a08f5ed26c0e816cb20d80a0b1411a9bc35da8a60f","54fdc6b142aee0e35a7428624a371b8cac5ddfc5b50ad5aef58a657366cbd207"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c38ec29c9d59f62cbcc1da88d6667b66b187a8acd5a61486c624d67b1079ea74"}51{"parentUuid":"04212b64-de7c-49f4-85ac-62bd825ce85f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsL8x3wrJvsA71YtoFe","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1322,"cache_read_input_tokens":23763,"output_tokens":671,"output_tokens_details":{"thinking_tokens":14},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6257,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsL8biWB7WHfKywuH6W","requestRef":"d7a4c8ff-74b4-4ff3-993c-c577f4838820","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8fe945b6-c33b-4e7a-8279-55d4a688d95e","timestamp":"2026-10-30T05:27:50.398Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"8fe945b6-c33b-4e7a-8279-55d4a688d95e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsL8x3wrJvsA71YtoFe","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: I found only three minor issues and nothing that changes a reported value. The review is saved to `out/review/review.json`.\n\nI ran `out/analysis.py` on a scratch copy. It gives the same `results.json` byte for byte and prints the source lines it matched, so hard-coding isn't a concern. I checked every required value against the two extracts by hand. Each one is as printed, with the right sign and number of decimals, and comes from the table its key names: the 5 km standard error is Table 6's 0.210, not Table A19's 0.218, and the wage figures come from Table 2, not Table 3. The extracts were the only inputs used. The two derived quantities are correct and state their critical value of 1.96, with 2.0 given as a sensitivity check:\n- **Confidence interval for the 5 km effect:** [-0.8426, -0.0194].\n- **Standard error for the wage effect:** 503.8265.\n\nThe HIV 5 km p-value is correctly left empty because the source doesn't print it, and the effect is correctly marked as not significant at 10%. Calling the task \"complete\" is justified. The line counts match `wc -l`. The flagged source problems are real (608 vs 640 control regions, the 0.210 vs 0.218 standard error, unsigned narrative bounds on the weeks-on-UI interval), and the claims are backed by the numbers with suitable caveats.\n\nThe three minor issues:\n1. **HIV wording:** the narrative calling the HIV estimates \"close to zero\" is listed as a contradiction in the source. With an estimate of -0.340, a standard error of 0.294 and no significance star, that is a loose description rather than a clear inconsistency.\n2. **Extra keys:** the `baseline_*` and `check_*` entries sit at the top level of `results.json`, outside the required schema. They are clearly labelled and harmless.\n3. **Control-region count:** Table A19's own body says 608; only its note says 640. The data issue could say this more precisely."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1322,"cache_read_input_tokens":23763,"output_tokens":671,"output_tokens_details":{"thinking_tokens":14},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1322},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsL8biWB7WHfKywuH6W","requestRef":"d7a4c8ff-74b4-4ff3-993c-c577f4838820","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0e53665a-18e7-4203-9091-7fea4d2b0c09","timestamp":"2026-10-30T05:27:50.401Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0283","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","version":"2.1.314","gitBranch":"HEAD"}53{"type":"last-prompt","lastPrompt":"Review","leafUuid":"0e53665a-18e7-4203-9091-7fea4d2b0c09","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}54{"type":"atis-latch","atis":"","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c"}55{"type":"cost-state","sessionId":"6ab152be-b2cd-455c-9124-d3f247276d7c","totalCostUSD":0.20605059999999997,"totalAPIDuration":37114,"totalAPIDurationWithoutRetries":37108,"totalToolDuration":235,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":37487,"startTime":1793338032921,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":3180,"thinkingTokens":573,"cacheReadInputTokens":122368,"cacheCreationInputTokens":23581,"cacheCreationEphemeral5mInputTokens":23581,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.20605059999999997}},"hasUnknownModelCost":false}56