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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d"}12{"parentUuid":"83047577-fa3c-4f0e-ad73-8553a5db92cc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXniRQXWS9wsBUzkJQJj","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":125,"output_tokens_details":{"thinking_tokens":19},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":563,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXniR1E9ngXDfE1RoaG3","requestRef":"896ea9ea-e71b-4470-a1b6-ea63802ceb8d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c76a9f2d-9400-4587-b95b-390da1861666","timestamp":"2026-10-30T04:27:12.404Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"c76a9f2d-9400-4587-b95b-390da1861666","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXniRQXWS9wsBUzkJQJj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0169Q7NnT98oDuSsMBv6Zjqo","name":"Bash","input":{"command":"cat TASK.md; 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For every assertion, decide from the cited extract alone whether it is **supported** (every element of the assertion — sign, magnitude, uncertainty statement, significance claim, population — is stated in, or follows arithmetically from, the source), **not_supported** (at least one element is contradicted by the source or does not follow from it), or **not_assessable** (the source does not report the quantity or comparison needed to judge the assertion, so it can be neither confirmed nor refuted from the extract). Each verdict must carry a locator (source id and the table, row/column or section that decides it) and a one-sentence reason. The estimands are the six verdicts.\n\n## Inputs\n- `data/assertions.json` — list of the six assertions: {\"id\", \"cites\" (source id), \"text\"}.\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: abstract, Section 6 (Results: Table 6 overall ATT by buffer; Table 7 ATT by outcome category and buffer; Section 6.1 Mechanisms) and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants (ATTs in log points; the \"% change\" rows and the text convert them to percentage changes); standard errors in parentheses, clustered at the MedStat region level; stars: *** 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: abstract, Section 3 Results (Table 2 with group means, treatment–control differences and ATE estimates with p-values and 95% CIs; results text; Section 3.1) and Table 3. Tables 2 and 3 were re-set one row per outcome (values verbatim; see the extract header).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. Do not use remembered content of the papers, other versions, or outside knowledge of the programmes.\n- A percentage change implied by a log-point coefficient b is 100·(exp(b) − 1); a 95% confidence interval from a printed standard error is the estimate ± 1.96·SE (2.0 is also acceptable). Use these conventions when an assertion requires a conversion the source does not print.\n- \"Supported\" requires every element of the assertion to hold; an assertion that is partly correct and partly contradicted is `not_supported`. Use `not_assessable` only when the extract contains no quantity or comparison bearing on the claim (e.g. a subgroup the source never reports), not when the claim is merely imprecise.\n- A locator has the form `<source id> | Table <n> | row \"<row>\" | column \"<col>\"` or `<source id> | Section <n or title>`; one locator per assertion.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `verdicts` | object | — | {\"A1\": \"supported\" / \"not_supported\" / \"not_assessable\", …, \"A6\": …} |\n| `locators` | object | — | {\"A1\": \"<locator>\", …} — the table/section that decides the verdict (for `not_assessable`, the table/section where the relevant outcome is reported, or \"not reported in <source id>\") |\n| `reasons` | object | — | {\"A1\": \"<one sentence naming the decisive element>\", …} |\n| `n_supported`, `n_not_supported`, `n_not_assessable` | integer | — | counts over the six assertions |\n| `task_id` | string | — | \"F4-conf-02\" |\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. \"Uncertainty\" must discuss which verdicts depend on a convention (critical value, log-to-percent conversion) and whether the verdict would change under a reasonable alternative.\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 (the script may encode the verdict logic explicitly; it must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 .\ndrwxr-xr-x 63 simon-pierreboucher staff 2016 oct. 30 00:27 ..\n-rw-r--r-- 1 simon-pierreboucher staff 737 oct. 29 23:45 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 data\n-rw-r--r-- 1 simon-pierreboucher staff 1547 oct. 29 23:45 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 6080 oct. 29 23:45 TASK.md\ndata:\nassertions.json\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","is_error":false}]},"uuid":"49c97d43-b807-4073-9aec-dae97fb91725","timestamp":"2026-10-30T04:27:13.041Z","toolUseResult":{"stdout":"# F4-conf-02 — Is each quoted assertion supported by the source it cites?\n\n## Scientific question\nA literature review drafted from two open-access studies contains six assertions, each citing one of the two sources provided. For every assertion, decide from the cited extract alone whether it is **supported** (every element of the assertion — sign, magnitude, uncertainty statement, significance claim, population — is stated in, or follows arithmetically from, the source), **not_supported** (at least one element is contradicted by the source or does not follow from it), or **not_assessable** (the source does not report the quantity or comparison needed to judge the assertion, so it can be neither confirmed nor refuted from the extract). Each verdict must carry a locator (source id and the table, row/column or section that decides it) and a one-sentence reason. The estimands are the six verdicts.\n\n## Inputs\n- `data/assertions.json` — list of the six assertions: {\"id\", \"cites\" (source id), \"text\"}.\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: abstract, Section 6 (Results: Table 6 overall ATT by buffer; Table 7 ATT by outcome category and buffer; Section 6.1 Mechanisms) and Appendix Table A19. Outcomes are log hospitalisation rates per 100,000 inhabitants (ATTs in log points; the \"% change\" rows and the text convert them to percentage changes); standard errors in parentheses, clustered at the MedStat region level; stars: *** 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: abstract, Section 3 Results (Table 2 with group means, treatment–control differences and ATE estimates with p-values and 95% CIs; results text; Section 3.1) and Table 3. Tables 2 and 3 were re-set one row per outcome (values verbatim; see the extract header).\n- `data/README.md` — provenance, retrieval times, licences, SHA-256 of the extracts.\n\n## Information-availability rules\n- The extracts are the only admissible evidence. Do not use remembered content of the papers, other versions, or outside knowledge of the programmes.\n- A percentage change implied by a log-point coefficient b is 100·(exp(b) − 1); a 95% confidence interval from a printed standard error is the estimate ± 1.96·SE (2.0 is also acceptable). Use these conventions when an assertion requires a conversion the source does not print.\n- \"Supported\" requires every element of the assertion to hold; an assertion that is partly correct and partly contradicted is `not_supported`. Use `not_assessable` only when the extract contains no quantity or comparison bearing on the claim (e.g. a subgroup the source never reports), not when the claim is merely imprecise.\n- A locator has the form `<source id> | Table <n> | row \"<row>\" | column \"<col>\"` or `<source id> | Section <n or title>`; one locator per assertion.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `verdicts` | object | — | {\"A1\": \"supported\" / \"not_supported\" / \"not_assessable\", …, \"A6\": …} |\n| `locators` | object | — | {\"A1\": \"<locator>\", …} — the table/section that decides the verdict (for `not_assessable`, the table/section where the relevant outcome is reported, or \"not reported in <source id>\") |\n| `reasons` | object | — | {\"A1\": \"<one sentence naming the decisive element>\", …} |\n| `n_supported`, `n_not_supported`, `n_not_assessable` | integer | — | counts over the six assertions |\n| `task_id` | string | — | \"F4-conf-02\" |\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. \"Uncertainty\" must discuss which verdicts depend on a convention (critical value, log-to-percent conversion) and whether the verdict would change under a reasonable alternative.\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 (the script may encode the verdict logic explicitly; it must print the source lines it relies on).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available; the standard library is sufficient for this task). Do not modify `data/`.\n- The source extracts under `data/sources/` are the **only** admissible evidence. Do not rely on memory of the papers or on other versions of them; if a quantity is not in the extract, it is not available.\n- Report numbers exactly as printed in the source (same sign, same number of decimals); do not round or rescale printed values unless the key definition asks for a transformation.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:45 .\ndrwxr-xr-x 63 simon-pierreboucher staff 2016 oct. 30 00:27 ..\n-rw-r--r-- 1 simon-pierreboucher staff 737 oct. 29 23:45 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:45 data\n-rw-r--r-- 1 simon-pierreboucher staff 1547 oct. 29 23:45 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 6080 oct. 29 23:45 TASK.md\ndata:\nassertions.json\nREADME.md\nsources\n\ndata/sources:\narxiv-2606.14621v1.txt\narxiv-2609.27732v1.txt","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bab99e88-464f-43d8-a855-719aa51f9886","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","hash":"de10cc492edcb92a22795242d4604f00e0fbf45ca89271b82da987a3ddf69080","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0169Q7NnT98oDuSsMBv6Zjqo","name":"Bash","input":{"command":"cat TASK.md; 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Each extract begins with a provenance header (title, authors, bibliographic record, retrieval URL and UTC, SHA-256 of the retrieved HTML, license, list of included and omitted sections). Only the excerpts needed for the task are reproduced; everything else is omitted. Tables are rendered one row per line with ` | ` between cells; in regression tables, the line following a coefficient row holds the standard errors in parentheses as printed in the original.\n\n| file | document | retrieval UTC | license | SHA-256 of the extract |\n|---|---|---|---|---|\n| `sources/arxiv-2609.27732v1.txt` | Armendariz, A. (2026). Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland. arXiv:2609.27732v1 [econ.GN]. https://arxiv.org/abs/2609.27732v1 (full text: https://arxiv.org/html/2609.27732v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b |\n| `sources/arxiv-2606.14621v1.txt` | Li, H. H., Pearson-Merkowitz, S., & Yokum, D. (2026). Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes. arXiv:2606.14621v1 [econ.GN]. https://arxiv.org/abs/2606.14621v1 (full text: https://arxiv.org/html/2606.14621v1) | 2026-10-30T03:23:18Z | CC BY 4.0 | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 |\n\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `assertions.json` — the six assertions to be checked (id, cited source id, text), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. No numbers were altered; where a table was re-set for legibility (RESEA Tables 2-3, London Table 3 header) the extract header says so.\n{\n \"task_id\": \"F4-conf-02\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\"\n ],\n \"results_keys\": {\n \"verdicts\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"locators\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"reasons\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_supported\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_not_supported\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_not_assessable\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"feasibility_reason\": {\n \"type\": \"string\",\n \"nullable\": true\n },\n \"data_issues\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"n_input_rows\": {\n \"type\": \"object\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": true\n },\n \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\"\n ],\n \"ci_pairs\": [],\n \"positive_keys\": [],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}\n{\n \"task_id\": \"F4-conf-02\",\n \"family\": \"F4\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:45:06.399097+00:00\",\n \"files\": {\n \"TASK.md\": \"2f517ee00f69f6ba411c5189a9358a21afc763b36c88f8420603c079421a7c1a\",\n \"data/README.md\": \"5348bd49912099735108dbbe19ae09fa72bd600cb3d12b878658e6b9806198a7\",\n \"data/assertions.json\": \"2965ab3acc6f326653169b83a5345698b51fac88a556de65faf89210aea0cad4\",\n \"data/sources/arxiv-2606.14621v1.txt\": \"27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238\",\n \"data/sources/arxiv-2609.27732v1.txt\": \"ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b\",\n \"gate_spec.json\": \"c7b67c16ad001e747b68be8a277a0e635f35013ccc3d44ef0f0a6270ec28991c\"\n }\n} 32 data/assertions.json\n 14 data/README.md\n 58 data/sources/arxiv-2606.14621v1.txt\n 134 data/sources/arxiv-2609.27732v1.txt\n 238 total","is_error":false}]},"uuid":"807b9a19-6d16-4d00-8f1c-e9ebba096172","timestamp":"2026-10-30T04:27:14.941Z","toolUseResult":{"stdout":"[\n {\n \"id\": \"A1\",\n \"cites\": \"arxiv-2609.27732v1\",\n \"text\": \"Within 5 km of a drug consumption room, the overall ATT on the log drug-related hospitalisation rate is −0.431 (SE 0.210), statistically significant at the 5% level, which corresponds to a 35.0% reduction in the hospitalisation rate.\"\n },\n {\n \"id\": \"A2\",\n \"cites\": \"arxiv-2609.27732v1\",\n \"text\": \"At the 1 km buffer, the estimated ATT for HIV hospitalisations is negative (−0.063), i.e. HIV hospitalisations fall after a DCR opening in the immediate vicinity.\"\n },\n {\n \"id\": \"A3\",\n \"cites\": \"arxiv-2609.27732v1\",\n \"text\": \"The 3 km ATT of −0.283 implies a 28.3% reduction in drug-related hospitalisations within 3 km of a DCR.\"\n },\n {\n \"id\": \"A4\",\n \"cites\": \"arxiv-2609.27732v1\",\n \"text\": \"For viral hepatitis at the 5 km buffer, the 95% confidence interval of the ATT is approximately [−0.66, −0.16] and therefore excludes zero.\"\n },\n {\n \"id\": \"A5\",\n \"cites\": \"arxiv-2609.27732v1\",\n \"text\": \"The reduction in drug-related hospitalisations after a DCR opening is larger for men than for women.\"\n },\n {\n \"id\": \"A6\",\n \"cites\": \"arxiv-2606.14621v1\",\n \"text\": \"Selection into RESEA reduced the number of weeks on UI by 1.99 weeks (95% CI [−2.24, −1.75]), an 11.5% decrease relative to the control-group mean of 17.3 weeks.\"\n }\n]\n# Data provenance — F4-conf-02\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\nTask-specific input files (prepared by the task designer, not observed data):\n\n- `assertions.json` — the six assertions to be checked (id, cited source id, text), written by the task designer\n\nRights: arXiv e-prints are reproduced in part under the Creative Commons Attribution 4.0 licence shown on their arXiv abstract pages. 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Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false}]},"uuid":"9d1d018c-f6da-41f2-8a80-fcd3a3b9f5c7","timestamp":"2026-10-30T04:27:17.170Z","toolUseResult":{"stdout":" 1\tSOURCE EXTRACT — arxiv-2609.27732v1\n 2\tTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"28b26792-3ff8-4bc5-9656-6452d4d36112","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"9d1d018c-f6da-41f2-8a80-fcd3a3b9f5c7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnihLEZjDYm78kpJQ8F","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016BBbH5erszjjXJtbMzhSUe","name":"Bash","input":{"command":"cat -n data/sources/arxiv-2606.14621v1.txt; shasum -a 256 data/*.* data/sources/*","description":"Read RESEA source extract and hashes"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2776,"cache_read_input_tokens":6952,"output_tokens":218,"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":2776},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnih3dZaHnPL3hNfcAm","requestRef":"60a434ee-6ddf-43f4-a08c-5e4069577fd1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2db9c23d-ec1d-445f-9825-60915d2046f7","timestamp":"2026-10-30T04:27:17.659Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"2db9c23d-ec1d-445f-9825-60915d2046f7","isSidechain":false,"promptId":"c4ca9a78-d9a9-4be5-86a3-00a6e30532d1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016BBbH5erszjjXJtbMzhSUe","type":"tool_result","content":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n2965ab3acc6f326653169b83a5345698b51fac88a556de65faf89210aea0cad4 data/assertions.json\n5348bd49912099735108dbbe19ae09fa72bd600cb3d12b878658e6b9806198a7 data/README.md\n27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 data/sources/arxiv-2606.14621v1.txt\nee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b data/sources/arxiv-2609.27732v1.txt","is_error":false}]},"uuid":"aa586755-297e-4bcd-9c2e-9da08b4372a4","timestamp":"2026-10-30T04:27:17.698Z","toolUseResult":{"stdout":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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Evidence from Switzerland\n 3\tAuthors: Ana Armendariz (University of St. Gallen)\n 4\tBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\n 5\tRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\n 8\tOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\t================================================================\n 11\t\n 12\t# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n 13\t\n 14\tAna Armendariz*\n 15\t\n 16\tSeptember 23, 2026\n 17\t\n 18\tThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n 19\t\n 20\tKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n 21\t\n 22\t## 6 Results\n 23\t\n 24\tThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n 25\t\n 26\tFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n 27\t\n 28\tNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n 29\t\n 30\tFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n 31\t\n 32\tTable 6: Overall Average Treatment Effect on the Treated (ATT)\n 33\t\n 34\t| 1 km | 3 km | 5 km\n 35\t\n 36\tATT | -0.399 | -0.283 | -0.431**\n 37\t\n 38\t| (0.412) | (0.279) | (0.210)\n 39\t\n 40\t% change | -32.9% | -24.7% | -35.0%\n 41\t\n 42\tTreated regions | 7 | 35 | 50\n 43\t\n 44\tControl regions | 608 | 608 | 608\n 45\t\n 46\tObservations | 16,175 | 16,875 | 17,250\n 47\t\n 48\tNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\n 49\tthroughout. Regions near DCRs that opened before 1998\n 50\t(always-treated) are excluded. Standard errors clustered at the Medstat region level in\n 51\tparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n 52\t{}^{*}p<0.10 .\n 53\t\n 54\tTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\n 55\tcorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n 56\t\n 57\tTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\n 58\tsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n 59\t\n 60\tFigure 7: Event study: Sub-outcome estimates,\n 61\twithin 5 km, \\pm 5-year window\n 62\t\n 63\tNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\n 64\tDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n 65\t\n 66\tTable 7: Overall ATT by Outcome Category and Buffer Zone\n 67\t\n 68\tOutcome | 1 km | 3 km | 5 km\n 69\t\n 70\tMental/Behavioral | -0.354 | -0.202 | -0.381*\n 71\t\n 72\t| (0.382) | (0.244) | (0.220)\n 73\t\n 74\tHepatitis | -0.270 | -0.413 | -0.406\n 75\t\n 76\t| (0.486) | (0.270) | (0.250)\n 77\t\n 78\tHIV | 0.063 | -0.239 | -0.340\n 79\t\n 80\t| (0.801) | (0.337) | (0.294)\n 81\t\n 82\tNarcotics | 1.186*** | 0.549*** | 0.390**\n 83\t\n 84\t| (0.288) | (0.187) | (0.165)\n 85\t\n 86\tDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n 87\t\n 88\t| (0.137) | (0.215) | (0.178)\n 89\t\n 90\tTreated regions | 7 | 35 | 50\n 91\t\n 92\tControl regions | 608 (never-treated) | |\n 93\t\n 94\tNote: Each cell reports the overall ATT from\n 95\tthe doubly robust Callaway & Sant’Anna (2021) estimator with\n 96\tnever-treated controls and a universal base period. Outcomes are\n 97\tlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\n 98\tmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\n 99\tHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\n 100\testimates are identified from a single treated region per cohort and\n 101\tshould be interpreted with caution. Regions near DCRs opened before\n 102\t1998 excluded. Standard errors clustered at the Medstat region level.\n 103\tSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n 104\t\n 105\tFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n 106\t\n 107\t### 6.1 Mechanisms\n 108\t\n 109\tThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\n 110\tsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n 111\t(Kerr et al., 2017; Hedrich, 2004).\n 112\t\n 113\tThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n 114\t\n 115\tA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n 116\t\n 117\tTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n 118\t\n 119\t| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n 120\t\n 121\tATT | -0.431** | -0.438** | -0.362* | -0.149***\n 122\t\n 123\t| (0.218) | (0.208) | (0.205) | (0.038)\n 124\t\n 125\t% change | -35.0% | -35.5% | -30.3% | -13.9%\n 126\t\n 127\tTreated regions | 50 | | |\n 128\t\n 129\tControl regions | 608 | | |\n 130\t\n 131\tObservations | 17,250 | | |\n 132\t\n 133\tNote: All specifications use the 5 km buffer,\n 134\tnever-treated comparison regions (640 regions), and a \\pm 5-year window","is_error":false},{"tool_use_id":"toolu_016BBbH5erszjjXJtbMzhSUe","type":"tool_result","content":" 1\tSOURCE EXTRACT — arxiv-2606.14621v1\n 2\tTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 3\tAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\n 4\tBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\n 5\tRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\n 6\tLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\n 7\tExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\n 8\tOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\n 9\tRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n 10\tEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n 11\t================================================================\n 12\t\n 13\t# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n 14\t\n 15\tHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n 16\t\n 17\t###### Abstract\n 18\t\n 19\tProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n 20\t\n 21\t## 3 Results\n 22\t\n 23\t[Table 1 (pre-treatment characteristics) omitted from this extract.]\n 24\t\n 25\t[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n 26\t\n 27\tOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\n 28\tWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\n 29\tReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\n 30\tWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n 31\tShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\n 32\tShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\n 33\tShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n 34\t\n 35\tTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n 36\t\n 37\tOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n 38\t\n 39\tThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n 40\t\n 41\tAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n 42\t\n 43\t### 3.1 Cost-benefit analysis\n 44\t\n 45\tWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n 46\t\n 47\t### 4.1 Methodological lessons (excerpt)\n 48\t\n 49\t[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n 50\t\n 51\tOutcome | Difference in means | Covariate adjusted | Fixed effects regression\n 52\tWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\n 53\tReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\n 54\tWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n 55\t\n 56\tTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n 57\t\n 58\tIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-02\n**Question.** For six assertions in a draft literature review, each citing one of two arXiv extracts, decide from the cited extract alone whether the assertion is supported / not_supported / not_assessable.\n**Estimands.** Six categorical verdicts (A1..A6), each with one locator and a one-sentence reason. 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F4-conf-02\n**Question.** For six assertions in a draft literature review, each citing one of two arXiv extracts, decide from the cited extract alone whether the assertion is supported / not_supported / not_assessable.\n**Estimands.** Six categorical verdicts (A1..A6), each with one locator and a one-sentence reason. 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RESEA outcomes in $, pp, weeks with p-values and 95% CIs.\nDate ranges: DCR openings 1998–2022; RESEA RCT Feb 2022–Sep 2023.\nAssertions A1–A5 cite arxiv-2609.27732v1; A6 cites arxiv-2606.14621v1. Both cited sources present.\n\n## Anomalies / internal inconsistencies (none blocks the verdicts)\n1. DCR Table A19 CS-DR column: same ATT −0.431 as Table 6 5 km, but SE (0.218) vs (0.210).\n2. DCR Table A19 note: \"640 regions\" never-treated vs 608 control regions in Table 6/7, A19 body, Figure 6 note.\n3. DCR abstract: fatal hospitalisations \"fall by 35%\"; Table 7 5 km fatal ATT −0.354 → 100(exp(−0.354)−1) = −29.8%, matching Section 6.1 \"approximately 30%\", not 35%.\n4. DCR: text says \"five diagnostic sub-categories\"; Figure 7 note says \"four\"; Table 7 note lists ICD codes for four only (fatal not coded).\n5. DCR results text says HIV estimates \"close to zero\" although the 5 km point estimate is −0.340 (SE 0.294) — interpretive, not numeric.\n6. RESEA line 39: rendering defect \"p ¡ 0.001\" (should read p < 0.001).\n7. RESEA results text prints the weeks-on-UI CI as magnitudes of reduction \"[1.75 weeks, 2.24 weeks]\" while Table 2/3 print signed [−2.24, −1.75]; equivalent.\n\n## Availability rules\nNo input violates the rules: both extracts are the admissible evidence; assertions.json is the object of assessment, not evidence. No external information used.\n\n## Exclusions\nNone: all 6 assertions assessed (see out/exclusions.csv).\nEOF\nprintf 'step,rule,n_before,n_after,n_removed,rationale\\n1,no exclusion (all assertions assessed),6,6,0,every assertion cites a source present in data/sources\\n' > out/exclusions.csv","description":"Write data audit and exclusion ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1987,"cache_read_input_tokens":20450,"output_tokens":1310,"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":1987},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnkJ35hsySGwRS817Jq","requestRef":"2aa3af19-36f7-4b94-bd0c-25f978d15b79","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e11bd3db-65e5-4702-bb7e-98f05c11f7f9","timestamp":"2026-10-30T04:27:49.695Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"e11bd3db-65e5-4702-bb7e-98f05c11f7f9","isSidechain":false,"promptId":"c4ca9a78-d9a9-4be5-86a3-00a6e30532d1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018Udwoo6CKnFbRFN82wBSqh","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"b2054f56-1176-494b-bb21-6995a94dce9f","timestamp":"2026-10-30T04:27:49.729Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e11bd3db-65e5-4702-bb7e-98f05c11f7f9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}40{"type":"api-request-blob","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","hash":"90521e70983ba59ed06f10acf7f66c0fa81eb9119042a15cb66ac7193455b520","message":{"role":"assistant","content":[{"type":"text","text":"Now the data audit and the analysis script."},{"type":"tool_use","id":"toolu_018Udwoo6CKnFbRFN82wBSqh","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | lines (wc -l) | items | matches README/bundle hash |\n|---|---|---|---|---|\n| data/assertions.json | 2965ab3acc6f326653169b83a5345698b51fac88a556de65faf89210aea0cad4 | 32 | 6 assertions (A1–A6, ids unique, keys id/cites/text, no missing) | yes (bundle) |\n| data/README.md | 5348bd49912099735108dbbe19ae09fa72bd600cb3d12b878658e6b9806198a7 | 14 | — | yes (bundle) |\n| data/sources/arxiv-2609.27732v1.txt | ee2d444d2f78f2af2ca2cf69b59a61fcd7ac5e10ea29e8ddcd67917bf07d6a8b | 134 | Tables 6, 7, A19 + text | yes (README) |\n| data/sources/arxiv-2606.14621v1.txt | 27c463c0a144d9d5a3916dc641f15b6029f6c68b5e574ad9074e7df1aeb0a238 | 58 | Tables 2, 3 + text | yes (README) |\n\nUnits: DCR outcomes are log hospitalisation rates per 100,000 (ATT in log points, SE in parentheses, clustered at MedStat region); RESEA outcomes in $, pp, weeks with p-values and 95% CIs.\nDate ranges: DCR openings 1998–2022; RESEA RCT Feb 2022–Sep 2023.\nAssertions A1–A5 cite arxiv-2609.27732v1; A6 cites arxiv-2606.14621v1. Both cited sources present.\n\n## Anomalies / internal inconsistencies (none blocks the verdicts)\n1. DCR Table A19 CS-DR column: same ATT −0.431 as Table 6 5 km, but SE (0.218) vs (0.210).\n2. DCR Table A19 note: \"640 regions\" never-treated vs 608 control regions in Table 6/7, A19 body, Figure 6 note.\n3. DCR abstract: fatal hospitalisations \"fall by 35%\"; Table 7 5 km fatal ATT −0.354 → 100(exp(−0.354)−1) = −29.8%, matching Section 6.1 \"approximately 30%\", not 35%.\n4. DCR: text says \"five diagnostic sub-categories\"; Figure 7 note says \"four\"; Table 7 note lists ICD codes for four only (fatal not coded).\n5. DCR results text says HIV estimates \"close to zero\" although the 5 km point estimate is −0.340 (SE 0.294) — interpretive, not numeric.\n6. RESEA line 39: rendering defect \"p ¡ 0.001\" (should read p < 0.001).\n7. RESEA results text prints the weeks-on-UI CI as magnitudes of reduction \"[1.75 weeks, 2.24 weeks]\" while Table 2/3 print signed [−2.24, −1.75]; equivalent.\n\n## Availability rules\nNo input violates the rules: both extracts are the admissible evidence; assertions.json is the object of assessment, not evidence. No external information used.\n\n## Exclusions\nNone: all 6 assertions assessed (see out/exclusions.csv).\nEOF\nprintf 'step,rule,n_before,n_after,n_removed,rationale\\n1,no exclusion (all assertions assessed),6,6,0,every assertion cites a source present in data/sources\\n' > out/exclusions.csv","description":"Write data audit and exclusion ledger"}}]}}41{"type":"api-request","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","id":"e3dd0b38-004d-480a-a3ee-62aa6331d5cb","timestamp":"2026-10-30T04:27:49.733Z","version":"2.1.314","querySource":"sdk","shapeHash":"5d93f61a313eab955cf36b1157ea7e7087af16e70d4356638850eab3080cc39a","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":"2aa3af19-36f7-4b94-bd0c-25f978d15b79","keep":10,"tail":["90521e70983ba59ed06f10acf7f66c0fa81eb9119042a15cb66ac7193455b520","654c1e3d2d470fe972a483c07e6e44eaa7dfea1183fac9d87d5ec9403910b15c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3f5eec0bd5a436a4cdc9e32334147f15a37a60ed0971c4e14ab7ae8207e3ccd7"}42{"parentUuid":"b2054f56-1176-494b-bb21-6995a94dce9f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnmG4nrX9Eg7tVMohHh","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1325,"cache_read_input_tokens":22437,"output_tokens":5495,"output_tokens_details":{"thinking_tokens":112},"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":1325},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1358,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnmFmvZS5m9EjGsYqXG","requestRef":"e3dd0b38-004d-480a-a3ee-62aa6331d5cb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9ebddd69-4378-41de-9f84-62d427db4679","timestamp":"2026-10-30T04:27:52.442Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"9ebddd69-4378-41de-9f84-62d427db4679","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnmG4nrX9Eg7tVMohHh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G33wvV35uSfMDktd28saFt","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F4-conf-02: verify six assertions against their cited source extracts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json deterministically (no randomness).\"\"\"\nimport json, math, os, re\n\nDATA = \"data\"\nSRC = {\"arxiv-2609.27732v1\": os.path.join(DATA, \"sources\", \"arxiv-2609.27732v1.txt\"),\n \"arxiv-2606.14621v1\": os.path.join(DATA, \"sources\", \"arxiv-2606.14621v1.txt\")}\nZ, Z_ALT = 1.96, 2.0\n\ndef lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\nassertions = json.load(open(os.path.join(DATA, \"assertions.json\"), encoding=\"utf-8\"))\nL = {k: lines(v) for k, v in SRC.items()}\nDCR, RES = L[\"arxiv-2609.27732v1\"], L[\"arxiv-2606.14621v1\"]\n\ndef find(src, pattern, start=0):\n \"\"\"Return (lineno 1-based, line) of first line matching regex at/after start.\"\"\"\n for i in range(start, len(src)):\n if re.search(pattern, src[i]):\n return i + 1, src[i]\n raise ValueError(pattern)\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\ndef num(s):\n return float(re.sub(r\"[^0-9.\\-]\", \"\", s.replace(\"−\", \"-\")))\n\ndef show(tag, ln, text):\n print(f\" [{tag} line {ln}] {text}\")\n\ndef pct(b):\n return 100 * (math.exp(b) - 1)\n\n# ---- parse DCR Table 6 ----\nt6, _ = find(DCR, r\"^Table 6:\")\nln_att, att_line = find(DCR, r\"^ATT \\|\", t6)\nln_se, se_line = ln_att + 2, DCR[ln_att + 1]\nln_pc, pc_line = find(DCR, r\"^% change \\|\", t6)\nt6_att = cells(att_line)[1:] # 1 km, 3 km, 5 km\nt6_se = [num(c) for c in cells(se_line)[1:]]\nt6_pc = cells(pc_line)[1:]\n# ---- parse DCR Table 7 ----\nt7, _ = find(DCR, r\"^Table 7:\")\ndef t7row(name):\n ln, row = find(DCR, r\"^\" + re.escape(name) + r\" \\|\", t7)\n se = DCR[ln + 1]\n return ln, row, cells(row)[1:], [num(c) for c in cells(se)[1:]], se\n\nverd, loc, reas, detail = {}, {}, {}, {}\n\nprint(\"A1 -- DCR Table 6, 5 km column\")\nshow(\"Table 6\", ln_att, att_line); show(\"Table 6 SE\", ln_se, se_line); show(\"Table 6\", ln_pc, pc_line)\nb5 = num(t6_att[2]); se5 = t6_se[2]; stars5 = t6_att[2].count(\"*\")\nz5 = abs(b5) / se5\nok = (t6_att[2].startswith(\"-0.431\") and se5 == 0.210 and stars5 == 2 and t6_pc[2] == \"-35.0%\"\n and round(pct(b5), 1) == -35.0)\nprint(f\" b={b5}, SE={se5}, stars={stars5} (** = p<0.05), |b|/SE={z5:.3f}, 100(exp(b)-1)={pct(b5):.2f}%\")\nverd[\"A1\"] = \"supported\" if ok else \"not_supported\"\nloc[\"A1\"] = 'arxiv-2609.27732v1 | Table 6 | row \"ATT\" | column \"5 km\"'\nreas[\"A1\"] = (\"Table 6 prints the 5 km ATT as -0.431** with SE (0.210), ** denoting p<0.05, and a % change of -35.0% \"\n \"(100(exp(-0.431)-1) = -35.0%), matching every element of the assertion.\")\ndetail[\"A1\"] = {\"b\": b5, \"se\": se5, \"z\": round(z5, 4), \"pct\": round(pct(b5), 2)}\n\nprint(\"A2 -- DCR Table 7, HIV row, 1 km column\")\nln, row, c, se, seline = t7row(\"HIV\")\nshow(\"Table 7\", ln, row); show(\"Table 7 SE\", ln + 2, seline)\nhiv1 = num(c[0])\nprint(f\" HIV 1 km ATT = {c[0]} (sign {'positive' if hiv1 > 0 else 'negative'}), SE={se[0]}\")\nverd[\"A2\"] = \"not_supported\" if hiv1 > 0 else \"supported\"\nloc[\"A2\"] = 'arxiv-2609.27732v1 | Table 7 | row \"HIV\" | column \"1 km\"'\nreas[\"A2\"] = (\"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the claimed \"\n \"negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\")\ndetail[\"A2\"] = {\"b\": hiv1, \"se\": se[0]}\n\nprint(\"A3 -- DCR Table 6, 3 km column\")\nshow(\"Table 6\", ln_att, att_line); show(\"Table 6\", ln_pc, pc_line)\nb3 = num(t6_att[1])\nprint(f\" b={b3}, printed % change={t6_pc[1]}, 100(exp(b)-1)={pct(b3):.2f}% (assertion: -28.3%)\")\nverd[\"A3\"] = \"not_supported\" if abs(pct(b3) - (-28.3)) > 0.5 else \"supported\"\nloc[\"A3\"] = 'arxiv-2609.27732v1 | Table 6 | row \"% change\" | column \"3 km\"'\nreas[\"A3\"] = (\"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.7%), not 28.3%, \"\n \"and the estimate (SE 0.279) is not significant, so the stated magnitude is contradicted.\")\ndetail[\"A3\"] = {\"b\": b3, \"se\": t6_se[1], \"pct\": round(pct(b3), 2), \"printed_pct\": t6_pc[1]}\n\nprint(\"A4 -- DCR Table 7, Hepatitis row, 5 km column\")\nln, row, c, se, seline = t7row(\"Hepatitis\")\nshow(\"Table 7\", ln, row); show(\"Table 7 SE\", ln + 2, seline)\nbh, sh = num(c[2]), se[2]\nci = (bh - Z * sh, bh + Z * sh); ci2 = (bh - Z_ALT * sh, bh + Z_ALT * sh)\ncrit_needed = abs(bh) / sh\nprint(f\" b={bh}, SE={sh}; 95% CI (1.96) = [{ci[0]:.3f}, {ci[1]:.3f}]; (2.0) = [{ci2[0]:.3f}, {ci2[1]:.3f}]; \"\n f\"asserted [-0.66,-0.16] = b +/- {(-0.16 - -0.66)/2:.2f} (=1.0 SE); CI excludes 0 only if crit < {crit_needed:.3f}\")\nverd[\"A4\"] = \"not_supported\" if ci[1] > 0 and ci2[1] > 0 else \"supported\"\nloc[\"A4\"] = 'arxiv-2609.27732v1 | Table 7 | row \"Hepatitis\" | column \"5 km\"'\nreas[\"A4\"] = (\"With ATT -0.406 and SE (0.250), the 95% CI is -0.406 +/- 1.96*0.250 = [-0.896, 0.084], which includes zero \"\n \"(the asserted [-0.66, -0.16] is only +/- 1 SE), and the estimate carries no significance star.\")\ndetail[\"A4\"] = {\"b\": bh, \"se\": sh, \"ci_196\": [round(ci[0], 3), round(ci[1], 3)],\n \"ci_200\": [round(ci2[0], 3), round(ci2[1], 3)], \"crit_to_exclude_zero\": round(crit_needed, 3)}\n\nprint(\"A5 -- sex heterogeneity in DCR extract\")\nhits = [(i + 1, l) for i, l in enumerate(DCR) if re.search(r\"\\b(men|women|male|female|sex|gender)\\b\", l, re.I)]\nprint(f\" lines mentioning men/women/male/female/sex/gender: {len(hits)}\")\nfor h in hits: show(\"match\", *h)\nverd[\"A5\"] = \"not_assessable\" if not hits else \"REVIEW\"\nloc[\"A5\"] = \"not reported in arxiv-2609.27732v1\"\nreas[\"A5\"] = (\"The extract reports ATTs only by buffer (Table 6), outcome category (Table 7) and estimator (Table A19) \"\n \"and never by sex, so a men-versus-women comparison can be neither confirmed nor refuted.\")\ndetail[\"A5\"] = {\"n_sex_mentions\": len(hits)}\n\nprint(\"A6 -- RESEA Table 2, Weeks on UI row\")\nln, row = find(RES, r\"^Weeks on UI \\|\")\nshow(\"Table 2\", ln, row)\nlt, txt = find(RES, r\"11\\.5% decrease\")\nshow(\"Results text\", lt, txt[txt.find(\"a 1.99 week\"):txt.find(\"17.3 weeks.\") + 11])\ncc = cells(row)\nctrl = num(cc[2].split(\"weeks\")[0]); ate = cc[4]\nate_b = num(ate.split(\";\")[0].replace(\"weeks\", \"\"))\nci6 = [num(x) for x in re.search(r\"\\[([^\\]]+)\\]\", ate).group(1).split(\",\")]\nrel = 100 * abs(ate_b) / ctrl\nprint(f\" control mean={ctrl}, ATE={ate_b}, CI={ci6}, |ATE|/control = {rel:.2f}%\")\nok6 = ate_b == -1.99 and ci6 == [-2.24, -1.75] and ctrl == 17.3 and round(rel, 1) == 11.5\nverd[\"A6\"] = \"supported\" if ok6 else \"not_supported\"\nloc[\"A6\"] = ('arxiv-2606.14621v1 | Table 2 | row \"Weeks on UI\" | column \"Average treatment effect estimate\"')\nreas[\"A6\"] = (\"Table 2 gives the ATE of RESEA selection on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) \"\n \"with a control mean of 17.3 weeks, and 1.99/17.3 = 11.5% as also stated in the results text.\")\ndetail[\"A6\"] = {\"ate\": ate_b, \"ci\": ci6, \"control_mean\": ctrl, \"rel_pct\": round(rel, 2)}\n\n# ---- naive baseline: do all numbers in the assertion appear verbatim in the cited source? ----\nbaseline = {}\nfor a in assertions:\n srctext = \"\\n\".join(L[a[\"cites\"]]).replace(\"−\", \"-\")\n nums = re.findall(r\"\\d+\\.\\d+\", a[\"text\"])\n baseline[a[\"id\"]] = \"all_numbers_found\" if all(n in srctext for n in nums) else \"some_number_missing\"\n\nids = [a[\"id\"] for a in assertions]\nassert ids == [\"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"]\nassert all(v in (\"supported\", \"not_supported\", \"not_assessable\") for v in verd.values())\n\ndata_issues = [\n {\"issue\": \"arxiv-2609.27732v1: Table A19 CS-DR column prints SE (0.218) for the same ATT -0.431 for which Table 6 (5 km) prints SE (0.210).\",\n \"handling\": \"A1 judged against Table 6 (the table the assertion describes); significance at 5% holds under either SE (|b|/SE = 2.05 or 1.98 > 1.96), noted in report.\"},\n {\"issue\": \"arxiv-2609.27732v1: Table A19 note says 640 never-treated comparison regions, whereas Tables 6, 7, A19 body and Figure 6 note say 608.\",\n \"handling\": \"Recorded; not decisive for any assertion.\"},\n {\"issue\": \"arxiv-2609.27732v1: abstract says fatal hospitalisations fall by 35%, but Table 7 5 km fatal ATT -0.354 implies -29.8%, matching Section 6.1 ('approximately 30%').\",\n \"handling\": \"Recorded; no assertion concerns fatal outcomes; tables preferred over abstract text.\"},\n {\"issue\": \"arxiv-2609.27732v1: text announces five diagnostic sub-categories, Figure 7 note says four, Table 7 note gives ICD codes for four.\",\n \"handling\": \"Recorded; not decisive for any assertion.\"},\n {\"issue\": \"arxiv-2606.14621v1: rendering defect 'p ¡ 0.001' in the short-term weeks-on-UI sentence (should read p < 0.001).\",\n \"handling\": \"Read as p < 0.001 (consistent with Table 2); not decisive.\"},\n {\"issue\": \"arxiv-2606.14621v1: results text prints the weeks-on-UI CI as reduction magnitudes [1.75, 2.24] while Tables 2 and 3 print signed [-2.24, -1.75].\",\n \"handling\": \"Treated as equivalent sign conventions; A6 judged against Table 2.\"},\n]\n\nres = {\n \"task_id\": \"F4-conf-02\",\n \"verdicts\": verd, \"locators\": loc, \"reasons\": reas,\n \"n_supported\": sum(v == \"supported\" for v in verd.values()),\n \"n_not_supported\": sum(v == \"not_supported\" for v in verd.values()),\n \"n_not_assessable\": sum(v == \"not_assessable\" for v in verd.values()),\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": data_issues,\n \"n_input_rows\": {\"assertions.json\": len(assertions), \"README.md\": wc_l(os.path.join(DATA, \"README.md\")),\n \"sources/arxiv-2609.27732v1.txt\": wc_l(SRC[\"arxiv-2609.27732v1\"]),\n \"sources/arxiv-2606.14621v1.txt\": wc_l(SRC[\"arxiv-2606.14621v1\"])},\n \"n_analysed\": len(assertions),\n \"random_seed\": None,\n \"baseline_verdicts_number_match\": baseline,\n \"computation_details\": detail,\n \"conventions\": {\"pct_change\": \"100*(exp(b)-1)\", \"ci_crit\": Z, \"ci_crit_alt\": Z_ALT},\n}\nos.makedirs(\"out\", exist_ok=True)\nwith open(os.path.join(\"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n json.dump(res, f, indent=2, ensure_ascii=False)\nprint(\"verdicts:\", verd)\nprint(\"counts: supported=%d not_supported=%d not_assessable=%d\" % (res[\"n_supported\"], res[\"n_not_supported\"], res[\"n_not_assessable\"]))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1325,"cache_read_input_tokens":22437,"output_tokens":5495,"output_tokens_details":{"thinking_tokens":112},"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":1325},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnmFmvZS5m9EjGsYqXG","requestRef":"e3dd0b38-004d-480a-a3ee-62aa6331d5cb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1a242a8b-f29c-4349-81c1-d06b5f1a8a2e","timestamp":"2026-10-30T04:28:32.582Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"1a242a8b-f29c-4349-81c1-d06b5f1a8a2e","isSidechain":false,"promptId":"c4ca9a78-d9a9-4be5-86a3-00a6e30532d1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G33wvV35uSfMDktd28saFt","type":"tool_result","content":"A1 -- DCR Table 6, 5 km column\n [Table 6 line 36] ATT | -0.399 | -0.283 | -0.431**\n [Table 6 SE line 38] | (0.412) | (0.279) | (0.210)\n [Table 6 line 40] % change | -32.9% | -24.7% | -35.0%\n b=-0.431, SE=0.21, stars=2 (** = p<0.05), |b|/SE=2.052, 100(exp(b)-1)=-35.01%\nA2 -- DCR Table 7, HIV row, 1 km column\n [Table 7 line 78] HIV | 0.063 | -0.239 | -0.340\n [Table 7 SE line 80] | (0.801) | (0.337) | (0.294)\n HIV 1 km ATT = 0.063 (sign positive), SE=0.801\nA3 -- DCR Table 6, 3 km column\n [Table 6 line 36] ATT | -0.399 | -0.283 | -0.431**\n [Table 6 line 40] % change | -32.9% | -24.7% | -35.0%\n b=-0.283, printed % change=-24.7%, 100(exp(b)-1)=-24.65% (assertion: -28.3%)\nA4 -- DCR Table 7, Hepatitis row, 5 km column\n [Table 7 line 74] Hepatitis | -0.270 | -0.413 | -0.406\n [Table 7 SE line 76] | (0.486) | (0.270) | (0.250)\n b=-0.406, SE=0.25; 95% CI (1.96) = [-0.896, 0.084]; (2.0) = [-0.906, 0.094]; asserted [-0.66,-0.16] = b +/- 0.25 (=1.0 SE); CI excludes 0 only if crit < 1.624\nA5 -- sex heterogeneity in DCR extract\n lines mentioning men/women/male/female/sex/gender: 0\nA6 -- RESEA Table 2, Weeks on UI row\n [Table 2 line 30] Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n [Results text line 37] 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.\n control mean=17.3, ATE=-1.99, CI=[-2.24, -1.75], |ATE|/control = 11.50%\nverdicts: {'A1': 'supported', 'A2': 'not_supported', 'A3': 'not_supported', 'A4': 'not_supported', 'A5': 'not_assessable', 'A6': 'supported'}\ncounts: supported=2 not_supported=3 not_assessable=1","is_error":false}]},"uuid":"7bdb5677-8709-4b85-9ef4-dd3aa1a63c3a","timestamp":"2026-10-30T04:28:32.631Z","toolUseResult":{"stdout":"A1 -- DCR Table 6, 5 km column\n [Table 6 line 36] ATT | -0.399 | -0.283 | -0.431**\n [Table 6 SE line 38] | (0.412) | (0.279) | (0.210)\n [Table 6 line 40] % change | -32.9% | -24.7% | -35.0%\n b=-0.431, SE=0.21, stars=2 (** = p<0.05), |b|/SE=2.052, 100(exp(b)-1)=-35.01%\nA2 -- DCR Table 7, HIV row, 1 km column\n [Table 7 line 78] HIV | 0.063 | -0.239 | -0.340\n [Table 7 SE line 80] | (0.801) | (0.337) | (0.294)\n HIV 1 km ATT = 0.063 (sign positive), SE=0.801\nA3 -- DCR Table 6, 3 km column\n [Table 6 line 36] ATT | -0.399 | -0.283 | -0.431**\n [Table 6 line 40] % change | -32.9% | -24.7% | -35.0%\n b=-0.283, printed % change=-24.7%, 100(exp(b)-1)=-24.65% (assertion: -28.3%)\nA4 -- DCR Table 7, Hepatitis row, 5 km column\n [Table 7 line 74] Hepatitis | -0.270 | -0.413 | -0.406\n [Table 7 SE line 76] | (0.486) | (0.270) | (0.250)\n b=-0.406, SE=0.25; 95% CI (1.96) = [-0.896, 0.084]; (2.0) = [-0.906, 0.094]; asserted [-0.66,-0.16] = b +/- 0.25 (=1.0 SE); CI excludes 0 only if crit < 1.624\nA5 -- sex heterogeneity in DCR extract\n lines mentioning men/women/male/female/sex/gender: 0\nA6 -- RESEA Table 2, Weeks on UI row\n [Table 2 line 30] Weeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\n [Results text line 37] 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.\n control mean=17.3, ATE=-1.99, CI=[-2.24, -1.75], |ATE|/control = 11.50%\nverdicts: {'A1': 'supported', 'A2': 'not_supported', 'A3': 'not_supported', 'A4': 'not_supported', 'A5': 'not_assessable', 'A6': 'supported'}\ncounts: supported=2 not_supported=3 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> out/analysis.py <<'EOF'\n\"\"\"F4-conf-02: verify six assertions against their cited source extracts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json deterministically (no randomness).\"\"\"\nimport json, math, os, re\n\nDATA = \"data\"\nSRC = {\"arxiv-2609.27732v1\": os.path.join(DATA, \"sources\", \"arxiv-2609.27732v1.txt\"),\n \"arxiv-2606.14621v1\": os.path.join(DATA, \"sources\", \"arxiv-2606.14621v1.txt\")}\nZ, Z_ALT = 1.96, 2.0\n\ndef lines(path):\n with open(path, encoding=\"utf-8\") as f:\n return f.read().split(\"\\n\")\n\ndef wc_l(path):\n with open(path, \"rb\") as f:\n return f.read().count(b\"\\n\")\n\nassertions = json.load(open(os.path.join(DATA, \"assertions.json\"), encoding=\"utf-8\"))\nL = {k: lines(v) for k, v in SRC.items()}\nDCR, RES = L[\"arxiv-2609.27732v1\"], L[\"arxiv-2606.14621v1\"]\n\ndef find(src, pattern, start=0):\n \"\"\"Return (lineno 1-based, line) of first line matching regex at/after start.\"\"\"\n for i in range(start, len(src)):\n if re.search(pattern, src[i]):\n return i + 1, src[i]\n raise ValueError(pattern)\n\ndef cells(line):\n return [c.strip() for c in line.split(\"|\")]\n\ndef num(s):\n return float(re.sub(r\"[^0-9.\\-]\", \"\", s.replace(\"−\", \"-\")))\n\ndef show(tag, ln, text):\n print(f\" [{tag} line {ln}] {text}\")\n\ndef pct(b):\n return 100 * (math.exp(b) - 1)\n\n# ---- parse DCR Table 6 ----\nt6, _ = find(DCR, r\"^Table 6:\")\nln_att, att_line = find(DCR, r\"^ATT \\|\", t6)\nln_se, se_line = ln_att + 2, DCR[ln_att + 1]\nln_pc, pc_line = find(DCR, r\"^% change \\|\", t6)\nt6_att = cells(att_line)[1:] # 1 km, 3 km, 5 km\nt6_se = [num(c) for c in cells(se_line)[1:]]\nt6_pc = cells(pc_line)[1:]\n# ---- parse DCR Table 7 ----\nt7, _ = find(DCR, r\"^Table 7:\")\ndef t7row(name):\n ln, row = find(DCR, r\"^\" + re.escape(name) + r\" \\|\", t7)\n se = DCR[ln + 1]\n return ln, row, cells(row)[1:], [num(c) for c in cells(se)[1:]], se\n\nverd, loc, reas, detail = {}, {}, {}, {}\n\nprint(\"A1 -- DCR Table 6, 5 km column\")\nshow(\"Table 6\", ln_att, att_line); show(\"Table 6 SE\", ln_se, se_line); show(\"Table 6\", ln_pc, pc_line)\nb5 = num(t6_att[2]); se5 = t6_se[2]; stars5 = t6_att[2].count(\"*\")\nz5 = abs(b5) / se5\nok = (t6_att[2].startswith(\"-0.431\") and se5 == 0.210 and stars5 == 2 and t6_pc[2] == \"-35.0%\"\n and round(pct(b5), 1) == -35.0)\nprint(f\" b={b5}, SE={se5}, stars={stars5} (** = p<0.05), |b|/SE={z5:.3f}, 100(exp(b)-1)={pct(b5):.2f}%\")\nverd[\"A1\"] = \"supported\" if ok else \"not_supported\"\nloc[\"A1\"] = 'arxiv-2609.27732v1 | Table 6 | row \"ATT\" | column \"5 km\"'\nreas[\"A1\"] = (\"Table 6 prints the 5 km ATT as -0.431** with SE (0.210), ** denoting p<0.05, and a % change of -35.0% \"\n \"(100(exp(-0.431)-1) = -35.0%), matching every element of the assertion.\")\ndetail[\"A1\"] = {\"b\": b5, \"se\": se5, \"z\": round(z5, 4), \"pct\": round(pct(b5), 2)}\n\nprint(\"A2 -- DCR Table 7, HIV row, 1 km column\")\nln, row, c, se, seline = t7row(\"HIV\")\nshow(\"Table 7\", ln, row); show(\"Table 7 SE\", ln + 2, seline)\nhiv1 = num(c[0])\nprint(f\" HIV 1 km ATT = {c[0]} (sign {'positive' if hiv1 > 0 else 'negative'}), SE={se[0]}\")\nverd[\"A2\"] = \"not_supported\" if hiv1 > 0 else \"supported\"\nloc[\"A2\"] = 'arxiv-2609.27732v1 | Table 7 | row \"HIV\" | column \"1 km\"'\nreas[\"A2\"] = (\"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the claimed \"\n \"negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\")\ndetail[\"A2\"] = {\"b\": hiv1, \"se\": se[0]}\n\nprint(\"A3 -- DCR Table 6, 3 km column\")\nshow(\"Table 6\", ln_att, att_line); show(\"Table 6\", ln_pc, pc_line)\nb3 = num(t6_att[1])\nprint(f\" b={b3}, printed % change={t6_pc[1]}, 100(exp(b)-1)={pct(b3):.2f}% (assertion: -28.3%)\")\nverd[\"A3\"] = \"not_supported\" if abs(pct(b3) - (-28.3)) > 0.5 else \"supported\"\nloc[\"A3\"] = 'arxiv-2609.27732v1 | Table 6 | row \"% change\" | column \"3 km\"'\nreas[\"A3\"] = (\"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.7%), not 28.3%, \"\n \"and the estimate (SE 0.279) is not significant, so the stated magnitude is contradicted.\")\ndetail[\"A3\"] = {\"b\": b3, \"se\": t6_se[1], \"pct\": round(pct(b3), 2), \"printed_pct\": t6_pc[1]}\n\nprint(\"A4 -- DCR Table 7, Hepatitis row, 5 km column\")\nln, row, c, se, seline = t7row(\"Hepatitis\")\nshow(\"Table 7\", ln, row); show(\"Table 7 SE\", ln + 2, seline)\nbh, sh = num(c[2]), se[2]\nci = (bh - Z * sh, bh + Z * sh); ci2 = (bh - Z_ALT * sh, bh + Z_ALT * sh)\ncrit_needed = abs(bh) / sh\nprint(f\" b={bh}, SE={sh}; 95% CI (1.96) = [{ci[0]:.3f}, {ci[1]:.3f}]; (2.0) = [{ci2[0]:.3f}, {ci2[1]:.3f}]; \"\n f\"asserted [-0.66,-0.16] = b +/- {(-0.16 - -0.66)/2:.2f} (=1.0 SE); CI excludes 0 only if crit < {crit_needed:.3f}\")\nverd[\"A4\"] = \"not_supported\" if ci[1] > 0 and ci2[1] > 0 else \"supported\"\nloc[\"A4\"] = 'arxiv-2609.27732v1 | Table 7 | row \"Hepatitis\" | column \"5 km\"'\nreas[\"A4\"] = (\"With ATT -0.406 and SE (0.250), the 95% CI is -0.406 +/- 1.96*0.250 = [-0.896, 0.084], which includes zero \"\n \"(the asserted [-0.66, -0.16] is only +/- 1 SE), and the estimate carries no significance star.\")\ndetail[\"A4\"] = {\"b\": bh, \"se\": sh, \"ci_196\": [round(ci[0], 3), round(ci[1], 3)],\n \"ci_200\": [round(ci2[0], 3), round(ci2[1], 3)], \"crit_to_exclude_zero\": round(crit_needed, 3)}\n\nprint(\"A5 -- sex heterogeneity in DCR extract\")\nhits = [(i + 1, l) for i, l in enumerate(DCR) if re.search(r\"\\b(men|women|male|female|sex|gender)\\b\", l, re.I)]\nprint(f\" lines mentioning men/women/male/female/sex/gender: {len(hits)}\")\nfor h in hits: show(\"match\", *h)\nverd[\"A5\"] = \"not_assessable\" if not hits else \"REVIEW\"\nloc[\"A5\"] = \"not reported in arxiv-2609.27732v1\"\nreas[\"A5\"] = (\"The extract reports ATTs only by buffer (Table 6), outcome category (Table 7) and estimator (Table A19) \"\n \"and never by sex, so a men-versus-women comparison can be neither confirmed nor refuted.\")\ndetail[\"A5\"] = {\"n_sex_mentions\": len(hits)}\n\nprint(\"A6 -- RESEA Table 2, Weeks on UI row\")\nln, row = find(RES, r\"^Weeks on UI \\|\")\nshow(\"Table 2\", ln, row)\nlt, txt = find(RES, r\"11\\.5% decrease\")\nshow(\"Results text\", lt, txt[txt.find(\"a 1.99 week\"):txt.find(\"17.3 weeks.\") + 11])\ncc = cells(row)\nctrl = num(cc[2].split(\"weeks\")[0]); ate = cc[4]\nate_b = num(ate.split(\";\")[0].replace(\"weeks\", \"\"))\nci6 = [num(x) for x in re.search(r\"\\[([^\\]]+)\\]\", ate).group(1).split(\",\")]\nrel = 100 * abs(ate_b) / ctrl\nprint(f\" control mean={ctrl}, ATE={ate_b}, CI={ci6}, |ATE|/control = {rel:.2f}%\")\nok6 = ate_b == -1.99 and ci6 == [-2.24, -1.75] and ctrl == 17.3 and round(rel, 1) == 11.5\nverd[\"A6\"] = \"supported\" if ok6 else \"not_supported\"\nloc[\"A6\"] = ('arxiv-2606.14621v1 | Table 2 | row \"Weeks on UI\" | column \"Average treatment effect estimate\"')\nreas[\"A6\"] = (\"Table 2 gives the ATE of RESEA selection on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) \"\n \"with a control mean of 17.3 weeks, and 1.99/17.3 = 11.5% as also stated in the results text.\")\ndetail[\"A6\"] = {\"ate\": ate_b, \"ci\": ci6, \"control_mean\": ctrl, \"rel_pct\": round(rel, 2)}\n\n# ---- naive baseline: do all numbers in the assertion appear verbatim in the cited source? ----\nbaseline = {}\nfor a in assertions:\n srctext = \"\\n\".join(L[a[\"cites\"]]).replace(\"−\", \"-\")\n nums = re.findall(r\"\\d+\\.\\d+\", a[\"text\"])\n baseline[a[\"id\"]] = \"all_numbers_found\" if all(n in srctext for n in nums) else \"some_number_missing\"\n\nids = [a[\"id\"] for a in assertions]\nassert ids == [\"A1\", \"A2\", \"A3\", \"A4\", \"A5\", \"A6\"]\nassert all(v in (\"supported\", \"not_supported\", \"not_assessable\") for v in verd.values())\n\ndata_issues = [\n {\"issue\": \"arxiv-2609.27732v1: Table A19 CS-DR column prints SE (0.218) for the same ATT -0.431 for which Table 6 (5 km) prints SE (0.210).\",\n \"handling\": \"A1 judged against Table 6 (the table the assertion describes); significance at 5% holds under either SE (|b|/SE = 2.05 or 1.98 > 1.96), noted in report.\"},\n {\"issue\": \"arxiv-2609.27732v1: Table A19 note says 640 never-treated comparison regions, whereas Tables 6, 7, A19 body and Figure 6 note say 608.\",\n \"handling\": \"Recorded; not decisive for any assertion.\"},\n {\"issue\": \"arxiv-2609.27732v1: abstract says fatal hospitalisations fall by 35%, but Table 7 5 km fatal ATT -0.354 implies -29.8%, matching Section 6.1 ('approximately 30%').\",\n \"handling\": \"Recorded; no assertion concerns fatal outcomes; tables preferred over abstract text.\"},\n {\"issue\": \"arxiv-2609.27732v1: text announces five diagnostic sub-categories, Figure 7 note says four, Table 7 note gives ICD codes for four.\",\n \"handling\": \"Recorded; not decisive for any assertion.\"},\n {\"issue\": 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code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 27 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] results_json: parsed\n [ok] key:verdicts: ok\n [ok] key:locators: ok\n [ok] key:reasons: ok\n [ok] key:n_supported: ok\n [ok] key:n_not_supported: ok\n [ok] key:n_not_assessable: ok\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0090/out/state/gate_receipts/20261030T042856.795014Z.json","is_error":true,"tool_use_id":"toolu_01YWhC7XiJfbg1rKonHjBZcS"}]},"uuid":"678c0cbe-22e7-4936-a62f-4b4e604d75b5","timestamp":"2026-10-30T04:28:56.802Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 27 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] results_json: parsed\n [ok] key:verdicts: ok\n [ok] key:locators: ok\n [ok] key:reasons: ok\n [ok] key:n_supported: ok\n [ok] key:n_not_supported: ok\n [ok] key:n_not_assessable: ok\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: 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pass |\n| 12 | Baseline (number-match) vs final: baseline flags A3, A4 as having unmatched numbers (agrees with not_supported); baseline misses A2 (sign error with matching digits) and cannot detect A5 — expected limitation of naive baseline | informative |\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed: hashes, line counts, anomalies recorded in data_audit.md; no exclusions.\n- 2026-10-30 P2 passed: naive number-match baseline stored under baseline_verdicts_number_match.\n- 2026-10-30 P3 passed: analysis.py implements verdict logic, prints source lines; verdicts 2 supported / 3 not_supported / 1 not_assessable.\n- 2026-10-30 Decision: A1 judged on Table 6 (SE 0.210) rather than Table A19 (SE 0.218); verdict invariant.\n- 2026-10-30 P4: clean re-run identical; checks.md written; running gate.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P1\"]={\"status\":\"passed\",\"checks\":[\"SHA-256 of all 4 inputs computed and matched README/bundle\",\"line/item counts recorded\",\"anomalies listed in data_audit.md\",\"exclusions.csv written (0 removed)\"],\"notes\":\"\"}\np[\"P2\"]={\"status\":\"passed\",\"checks\":[\"naive number-match baseline computed by analysis.py -> baseline_verdicts_number_match\"],\"notes\":\"\"}\np[\"P3\"]={\"status\":\"passed\",\"checks\":[\"analysis.py runs, reads only data/, prints decisive source lines, writes results.json\"],\"notes\":\"no randomness; random_seed null\"}\np[\"P4\"]={\"status\":\"running\",\"checks\":[\"clean re-run produced identical results.json hash\",\"12 consistency checks in checks.md all pass\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write checks, update ledger, run 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gate run: all P4-relevant checks pass (keys, reproducibility); the failures are P5 deliverables not yet written. Writing the report and claims now."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2016,"cache_read_input_tokens":31966,"output_tokens":3735,"output_tokens_details":{"thinking_tokens":60},"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":2016},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnrCXMEQPmanR9AsJGG","requestRef":"02790854-053b-4fb8-a9c8-76fee317c00e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d75dc523-5370-4725-862d-acdd4be7a19d","timestamp":"2026-10-30T04:28:58.988Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}76{"parentUuid":"d75dc523-5370-4725-862d-acdd4be7a19d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnrCpxqdJf7nvVc4VXR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018T8e4AMan2HuGkvwGkqKSD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-02 — Are the six assertions supported by their cited sources?\n\n## Question\nA draft literature review makes six assertions (A1–A6), five citing Armendariz (2026), arXiv:2609.27732v1 (drug consumption rooms, DCR) and one citing Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (Rhode Island RESEA RCT). For each one, using only the cited extract, I classify it as supported, not_supported or not_assessable, and give one locator and a one-sentence reason (`verdicts`, `locators`, `reasons`).\n\n## Data and exclusions\nInputs: `data/assertions.json` (6 items), `data/README.md` (14 lines), `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) (`n_input_rows`). The SHA-256 hashes match the README and bundle (`out/state/data_audit.md`). No assertion was excluded (`out/exclusions.csv`), so all 6 were assessed (`n_analysed` = 6).\nSix data issues were found and logged (`data_issues`). None of them changes a verdict:\n- Table A19 prints SE 0.218 for the CS-DR estimate −0.431, while Table 6 prints 0.210.\n- The Table A19 note gives 640 control regions; everywhere else the extract gives 608.\n- The abstract says fatal hospitalisations fall by \"35%\", but Table 7 (−0.354) implies −29.8%.\n- The text says there are \"five\" sub-categories, but the Figure 7 note says \"four\".\n- The RESEA text contains a rendering defect, \"p ¡ 0.001\".\n- The RESEA text prints the confidence interval (CI) as magnitudes of the reduction, [1.75, 2.24], while the tables print it with signs, [−2.24, −1.75].\n\n## Method\n`out/analysis.py` reads the extracts, parses the relevant table rows, prints the source lines it relies on, and applies the task's conventions: percentage change = 100·(exp(b) − 1); 95% CI = b ± 1.96·SE, with 2.0 checked as an alternative (`conventions`). The verdict logic is written into the script explicitly, and intermediate quantities are stored in `computation_details`. As a naive baseline, I checked whether every decimal number in each assertion appears verbatim in the cited source (`baseline_verdicts_number_match`). That baseline flags A3 and A4. It misses A2, where the digits match but the sign is wrong, and it cannot detect A5.\n\n## Results\nCounts: 2 supported, 3 not_supported, 1 not_assessable (`n_supported`, `n_not_supported`, `n_not_assessable`).\n\n| id | verdict (`verdicts`) | locator (`locators`) | decisive element (`reasons`, `computation_details`) |\n|---|---|---|---|\n| A1 | supported | arxiv-2609.27732v1, Table 6, row \"ATT\", col \"5 km\" | ATT −0.431**, SE (0.210), % change −35.0%; 100(exp(−0.431)−1) = −35.01% |\n| A2 | not_supported | arxiv-2609.27732v1, Table 7, row \"HIV\", col \"1 km\" | printed ATT is +0.063 (SE 0.801), not −0.063, so there is no fall |\n| A3 | not_supported | arxiv-2609.27732v1, Table 6, row \"% change\", col \"3 km\" | −0.283 implies −24.7% (printed and recomputed −24.65%), not 28.3% |\n| A4 | not_supported | arxiv-2609.27732v1, Table 7, row \"Hepatitis\", col \"5 km\" | −0.406 ± 1.96·0.250 = [−0.896, 0.084], which contains zero; the asserted interval is ±1 SE |\n| A5 | not_assessable | not reported in arxiv-2609.27732v1 | the extract has no results by sex (0 regex hits for men/women/male/female/sex/gender) |\n| A6 | supported | arxiv-2606.14621v1, Table 2, row \"Weeks on UI\", col \"Average treatment effect estimate\" | ATE −1.99 weeks, 95% CI [−2.24, −1.75], control mean 17.3; 1.99/17.3 = 11.50% |\n\n## Uncertainty\nThe verdicts are classifications, not estimates, so they carry no sampling uncertainty. Three verdicts depend on a convention:\n- **A1 (critical value / significance).** The assertion's \"significant at 5%\" matches the printed `**`. As a cross-check: |b|/SE = 2.05 with SE 0.210 (`computation_details.A1.z`), which is above both 1.96 and 2.0. If the Table A19 SE of 0.218 were used instead, the ratio would be 1.98. That is still above 1.96 but just below 2.0. Table A19 still prints `**`, and the assertion describes the Table 6 numbers, so the verdict does not change.\n- **A3 (log-to-percent conversion).** Converting gives −24.7% and the source prints −24.7%. The figure 28.3% would follow only from reading the log coefficient directly as a percentage, which the task's conventions rule out. Even that reading gives a 28.3% reduction only as a first-order approximation, and the source prints −24.7%. The verdict holds.\n- **A4 (critical value).** With 1.96 the CI is [−0.896, 0.084]; with 2.0 it is [−0.906, 0.094] (`computation_details.A4`). The CI would exclude zero only with a critical value below 1.624, i.e. a confidence level under about 90%. The verdict holds under any reasonable 95% convention.\n\nA2, A5 and A6 depend on no convention. A6 involves only printed values and a single division.\n\n## Limitations and open issues\n- The verdicts rest solely on the extracts, as the task requires. In particular, the A5 verdict of not_assessable is relative to the extract: the omitted sections of the paper might report results by sex, but they are inadmissible here.\n- The internal inconsistencies of the DCR source (SE 0.210 vs 0.218; 608 vs 640 controls; the abstract's 35% vs the table-implied ~30% for fatal outcomes) are reported in `data_issues` but not resolved. None of them decides a verdict.\n- The verdict logic is written into `out/analysis.py` (the task allows this), so the script documents and checks the judgements rather than deriving them automatically.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"A1 (5 km overall ATT −0.431, SE 0.210, significant at 5%, −35.0%) is supported by Table 6 of arxiv-2609.27732v1.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same estimate; significance at 5% still holds with 1.96.\"},\n {\"id\":\"C2\",\"text\":\"A2 is not supported: the 1 km HIV ATT in Table 7 is +0.063, not −0.063.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"A3 is not supported: a 3 km ATT of −0.283 implies a −24.7% change, not 28.3%.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Relies on the 100(exp(b)−1) convention, which the source itself uses.\"},\n {\"id\":\"C4\",\"text\":\"A4 is not supported: the 95% CI for the 5 km hepatitis ATT is [−0.896, 0.084] and includes zero.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Holds for critical values 1.96 or 2.0; would flip only below 1.624.\"},\n {\"id\":\"C5\",\"text\":\"A5 (larger reduction for men than for women) is not assessable because the DCR extract reports no results by sex.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"Relative to the extract only.\"},\n {\"id\":\"C6\",\"text\":\"A6 (RESEA ATE −1.99 weeks on UI, 95% CI [−2.24, −1.75], 11.5% of the 17.3-week control mean) is supported by Table 2 of arxiv-2606.14621v1.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Overall, 2 assertions are supported, 3 not supported and 1 not assessable, out of 6 assessed.\",\"results_keys\":[\"n_supported\",\"n_not_supported\",\"n_not_assessable\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C8\",\"text\":\"The inputs contain six minor issues (rendering defects and internal inconsistencies of the sources), none of which decides a verdict.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run produced identical results.json hash\",\"12 consistency checks in checks.md all pass\",\"gate run 1 receipt out/state/gate_receipts/20261030T042856.795014Z.json: all results-key and reproducibility checks ok; failures only for P5 deliverables (report, claims, terminal ledger) not yet written\"],\"notes\":\"\"}\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md written with 6 required sections citing results keys\",\"claims.json written (8 claims, all keys exist)\",\"gate run 2 at end of P5 (receipt recorded below after run)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1). P5: report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, update ledger, rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2016,"cache_read_input_tokens":31966,"output_tokens":3735,"output_tokens_details":{"thinking_tokens":60},"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":2016},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXnrCXMEQPmanR9AsJGG","requestRef":"02790854-053b-4fb8-a9c8-76fee317c00e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"60f930e7-dc01-43b1-9e1b-0fb8a91c6ba3","timestamp":"2026-10-30T04:29:27.918Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}77{"parentUuid":"60f930e7-dc01-43b1-9e1b-0fb8a91c6ba3","isSidechain":false,"promptId":"c4ca9a78-d9a9-4be5-86a3-00a6e30532d1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018T8e4AMan2HuGkvwGkqKSD","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 27 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\n [ok] results_json: parsed\n [ok] key:verdicts: ok\n [ok] key:locators: ok\n [ok] key:reasons: ok\n [ok] key:n_supported: ok\n [ok] key:n_not_supported: ok\n [ok] key:n_not_assessable: ok\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 8 claims, all keys resolve\n [ok] phase_ledger: consistent\n [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0090/out/state/gate_receipts/20261030T042928.035789Z.json","is_error":false}]},"uuid":"dcc32fa5-bac3-4233-ac6e-a19997854b36","timestamp":"2026-10-30T04:29:28.045Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 27 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\n [ok] results_json: parsed\n [ok] key:verdicts: ok\n [ok] key:locators: ok\n [ok] key:reasons: ok\n [ok] key:n_supported: ok\n [ok] key:n_not_supported: ok\n [ok] key:n_not_assessable: ok\n [ok] key:task_id: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 8 claims, all keys resolve\n [ok] phase_ledger: consistent\n [ok] reproducibility: regenerated in 0.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0090/out/state/gate_receipts/20261030T042928.035789Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"60f930e7-dc01-43b1-9e1b-0fb8a91c6ba3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}78{"type":"api-request-blob","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","hash":"b3bc0ec4722d1e702749d3d9f2658bd9ea32e44f838b7e079b7c7b922e9394c7","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4 gate run: all P4-relevant checks pass (keys, reproducibility); the failures are P5 deliverables not yet written. Writing the report and claims now."},{"type":"tool_use","id":"toolu_018T8e4AMan2HuGkvwGkqKSD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F4-conf-02 — Are the six assertions supported by their cited sources?\n\n## Question\nA draft literature review makes six assertions (A1–A6), five citing Armendariz (2026), arXiv:2609.27732v1 (drug consumption rooms, DCR) and one citing Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1 (Rhode Island RESEA RCT). For each one, using only the cited extract, I classify it as supported, not_supported or not_assessable, and give one locator and a one-sentence reason (`verdicts`, `locators`, `reasons`).\n\n## Data and exclusions\nInputs: `data/assertions.json` (6 items), `data/README.md` (14 lines), `data/sources/arxiv-2609.27732v1.txt` (134 lines), `data/sources/arxiv-2606.14621v1.txt` (58 lines) (`n_input_rows`). The SHA-256 hashes match the README and bundle (`out/state/data_audit.md`). No assertion was excluded (`out/exclusions.csv`), so all 6 were assessed (`n_analysed` = 6).\nSix data issues were found and logged (`data_issues`). None of them changes a verdict:\n- Table A19 prints SE 0.218 for the CS-DR estimate −0.431, while Table 6 prints 0.210.\n- The Table A19 note gives 640 control regions; everywhere else the extract gives 608.\n- The abstract says fatal hospitalisations fall by \"35%\", but Table 7 (−0.354) implies −29.8%.\n- The text says there are \"five\" sub-categories, but the Figure 7 note says \"four\".\n- The RESEA text contains a rendering defect, \"p ¡ 0.001\".\n- The RESEA text prints the confidence interval (CI) as magnitudes of the reduction, [1.75, 2.24], while the tables print it with signs, [−2.24, −1.75].\n\n## Method\n`out/analysis.py` reads the extracts, parses the relevant table rows, prints the source lines it relies on, and applies the task's conventions: percentage change = 100·(exp(b) − 1); 95% CI = b ± 1.96·SE, with 2.0 checked as an alternative (`conventions`). The verdict logic is written into the script explicitly, and intermediate quantities are stored in `computation_details`. As a naive baseline, I checked whether every decimal number in each assertion appears verbatim in the cited source (`baseline_verdicts_number_match`). That baseline flags A3 and A4. It misses A2, where the digits match but the sign is wrong, and it cannot detect A5.\n\n## Results\nCounts: 2 supported, 3 not_supported, 1 not_assessable (`n_supported`, `n_not_supported`, `n_not_assessable`).\n\n| id | verdict (`verdicts`) | locator (`locators`) | decisive element (`reasons`, `computation_details`) |\n|---|---|---|---|\n| A1 | supported | arxiv-2609.27732v1, Table 6, row \"ATT\", col \"5 km\" | ATT −0.431**, SE (0.210), % change −35.0%; 100(exp(−0.431)−1) = −35.01% |\n| A2 | not_supported | arxiv-2609.27732v1, Table 7, row \"HIV\", col \"1 km\" | printed ATT is +0.063 (SE 0.801), not −0.063, so there is no fall |\n| A3 | not_supported | arxiv-2609.27732v1, Table 6, row \"% change\", col \"3 km\" | −0.283 implies −24.7% (printed and recomputed −24.65%), not 28.3% |\n| A4 | not_supported | arxiv-2609.27732v1, Table 7, row \"Hepatitis\", col \"5 km\" | −0.406 ± 1.96·0.250 = [−0.896, 0.084], which contains zero; the asserted interval is ±1 SE |\n| A5 | not_assessable | not reported in arxiv-2609.27732v1 | the extract has no results by sex (0 regex hits for men/women/male/female/sex/gender) |\n| A6 | supported | arxiv-2606.14621v1, Table 2, row \"Weeks on UI\", col \"Average treatment effect estimate\" | ATE −1.99 weeks, 95% CI [−2.24, −1.75], control mean 17.3; 1.99/17.3 = 11.50% |\n\n## Uncertainty\nThe verdicts are classifications, not estimates, so they carry no sampling uncertainty. Three verdicts depend on a convention:\n- **A1 (critical value / significance).** The assertion's \"significant at 5%\" matches the printed `**`. As a cross-check: |b|/SE = 2.05 with SE 0.210 (`computation_details.A1.z`), which is above both 1.96 and 2.0. If the Table A19 SE of 0.218 were used instead, the ratio would be 1.98. That is still above 1.96 but just below 2.0. Table A19 still prints `**`, and the assertion describes the Table 6 numbers, so the verdict does not change.\n- **A3 (log-to-percent conversion).** Converting gives −24.7% and the source prints −24.7%. The figure 28.3% would follow only from reading the log coefficient directly as a percentage, which the task's conventions rule out. Even that reading gives a 28.3% reduction only as a first-order approximation, and the source prints −24.7%. The verdict holds.\n- **A4 (critical value).** With 1.96 the CI is [−0.896, 0.084]; with 2.0 it is [−0.906, 0.094] (`computation_details.A4`). The CI would exclude zero only with a critical value below 1.624, i.e. a confidence level under about 90%. The verdict holds under any reasonable 95% convention.\n\nA2, A5 and A6 depend on no convention. A6 involves only printed values and a single division.\n\n## Limitations and open issues\n- The verdicts rest solely on the extracts, as the task requires. In particular, the A5 verdict of not_assessable is relative to the extract: the omitted sections of the paper might report results by sex, but they are inadmissible here.\n- The internal inconsistencies of the DCR source (SE 0.210 vs 0.218; 608 vs 640 controls; the abstract's 35% vs the table-implied ~30% for fatal outcomes) are reported in `data_issues` but not resolved. None of them decides a verdict.\n- The verdict logic is written into `out/analysis.py` (the task allows this), so the script documents and checks the judgements rather than deriving them automatically.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"A1 (5 km overall ATT −0.431, SE 0.210, significant at 5%, −35.0%) is supported by Table 6 of arxiv-2609.27732v1.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints SE 0.218 for the same estimate; significance at 5% still holds with 1.96.\"},\n {\"id\":\"C2\",\"text\":\"A2 is not supported: the 1 km HIV ATT in Table 7 is +0.063, not −0.063.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"A3 is not supported: a 3 km ATT of −0.283 implies a −24.7% change, not 28.3%.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Relies on the 100(exp(b)−1) convention, which the source itself uses.\"},\n {\"id\":\"C4\",\"text\":\"A4 is not supported: the 95% CI for the 5 km hepatitis ATT is [−0.896, 0.084] and includes zero.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Holds for critical values 1.96 or 2.0; would flip only below 1.624.\"},\n {\"id\":\"C5\",\"text\":\"A5 (larger reduction for men than for women) is not assessable because the DCR extract reports no results by sex.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"Relative to the extract only.\"},\n {\"id\":\"C6\",\"text\":\"A6 (RESEA ATE −1.99 weeks on UI, 95% CI [−2.24, −1.75], 11.5% of the 17.3-week control mean) is supported by Table 2 of arxiv-2606.14621v1.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Overall, 2 assertions are supported, 3 not supported and 1 not assessable, out of 6 assessed.\",\"results_keys\":[\"n_supported\",\"n_not_supported\",\"n_not_assessable\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C8\",\"text\":\"The inputs contain six minor issues (rendering defects and internal inconsistencies of the sources), none of which decides a verdict.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run produced identical results.json hash\",\"12 consistency checks in checks.md all pass\",\"gate run 1 receipt out/state/gate_receipts/20261030T042856.795014Z.json: all results-key and reproducibility checks ok; failures only for P5 deliverables (report, claims, terminal ledger) not yet written\"],\"notes\":\"\"}\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md written with 6 required sections citing results keys\",\"claims.json written (8 claims, all keys exist)\",\"gate run 2 at end of P5 (receipt recorded below after run)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\necho \"- 2026-10-30 P4 passed (gate run 1). P5: report and claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, update ledger, rerun gate"}}]}}79{"type":"api-request","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","id":"8ddfc59b-fb0f-43a7-b650-7fcc51f12497","timestamp":"2026-10-30T04:29:28.048Z","version":"2.1.314","querySource":"sdk","shapeHash":"5d93f61a313eab955cf36b1157ea7e7087af16e70d4356638850eab3080cc39a","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":"02790854-053b-4fb8-a9c8-76fee317c00e","keep":22,"tail":["b3bc0ec4722d1e702749d3d9f2658bd9ea32e44f838b7e079b7c7b922e9394c7","34272ee7d059eeaa56adb3df51d0abbf09346f3dd001623e0cd12348430073a4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e258f5fda0e9a837dd5bc471396efddd2d1d48ff7e2f7d65c5b77c762bb66bd6"}80{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dcc32fa5-bac3-4233-ac6e-a19997854b36","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d"}81{"type":"atis-latch","atis":"","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d"}82{"parentUuid":"dcc32fa5-bac3-4233-ac6e-a19997854b36","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXntWPwXRknMWNYVfJFT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PA4E63sME75CKAM4J21Lt4","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P5\"][\"checks\"][2]=\"gate run 2 receipt out/state/gate_receipts/20261030T042928.035789Z.json: PASSED 27/27\"\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\necho \"- 2026-10-30 P5 passed: final gate PASSED (20261030T042928.035789Z). 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The final gate run passed all 27 checks, and re-running `out/analysis.py` reproduces `out/results.json` exactly.\n\n| id | verdict | what decides it |\n|---|---|---|\n| A1 | supported | Table 6, 5 km: the ATT is −0.431, SE 0.210, marked significant at 5%, printed as −35.0%. |\n| A2 | not supported | Table 7, HIV at 1 km: the paper prints **+0.063**, not −0.063, so HIV hospitalisations don't fall. |\n| A3 | not supported | Table 6, 3 km: −0.283 log points is a −24.7% change, not 28.3%. The assertion reads the log coefficient as a percentage. |\n| A4 | not supported | Table 7, hepatitis at 5 km: the 95% interval is −0.406 ± 1.96·0.250 = [−0.896, 0.084], which includes zero. The assertion's interval is only ±1 standard error. |\n| A5 | not assessable | The extract never reports results by sex, so the men-versus-women claim can be neither confirmed nor refuted. |\n| A6 | supported | Table 2 of the RESEA paper: −1.99 weeks on UI, 95% interval [−2.24, −1.75], control mean 17.3 weeks; 1.99/17.3 = 11.5%. |\n\n**Sensitivity to conventions:**\n- **A1:** the significance holds with the standard error printed in Table 6. The paper's robustness table (A19) prints 0.218 for the same estimate. With that value the estimate is still significant at 5% using 1.96, but falls just short using 2.0. I judged A1 on Table 6, which is the table the assertion describes.\n- **A4:** the hepatitis interval contains zero whether you use 1.96 or 2.0. It would exclude zero only with a critical value below 1.624.\n\n**Problems in the sources:** I logged six minor ones, and none changes a verdict. The most notable:\n- the two different standard errors for the same −0.431 estimate (0.210 vs 0.218);\n- 608 versus 640 control regions;\n- the abstract's 35% fall in fatal hospitalisations, where the table figure implies about 30%.\n\n**Limitations:**\n- The verdicts rely on the extracts alone, as the task requires. A5 is \"not assessable\" only relative to the extract; sections of the paper left out of it might report results by sex.\n- The verdict logic is written directly into `analysis.py`, which the task allows. So the script prints the source lines and checks the arithmetic, but the judgements themselves are mine.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, log, phase ledger, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":340,"cache_read_input_tokens":38246,"output_tokens":1003,"output_tokens_details":{"thinking_tokens":16},"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":340},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXntmQ5wAmQUh7Vj9Hu6","requestRef":"91457764-d629-4a35-8f45-6ef843cb2e67","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"235eeca4-ed08-464b-b4c8-31e044202891","timestamp":"2026-10-30T04:29:41.331Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0090","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","version":"2.1.314","gitBranch":"HEAD"}88{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"235eeca4-ed08-464b-b4c8-31e044202891","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d"}89{"type":"cost-state","sessionId":"bd9e5701-a7ed-48d6-b3a4-49027111208d","totalCostUSD":0.5791160000000001,"totalAPIDuration":149251,"totalAPIDurationWithoutRetries":149241,"totalToolDuration":911,"totalLinesAdded":1,"totalLinesRemoved":1,"totalDuration":150316,"startTime":1793334431023,"modelUsage":{"claude-opus-5-5":{"inputTokens":28,"outputTokens":16818,"thinkingTokens":1613,"cacheReadInputTokens":286170,"cacheCreationInputTokens":37082,"cacheCreationEphemeral5mInputTokens":37082,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5791160000000001}},"hasUnknownModelCost":false}90