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{"parentUuid":"c6a61355-3d41-46f7-bda3-61e20608fc5c","isSidechain":false,"promptId":"3fb0c257-bcd7-4b25-bc35-5818557e674d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01D5vm5FogrPaUN8WHeNFbjC","type":"tool_result","content":"# 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 72\ndrwxr-xr-x@ 9 simon-pierreboucher  staff   288 oct.  30 00:28 .\ndrwxr-xr-x  7 simon-pierreboucher  staff   224 oct.  30 00:26 ..\n-rw-r--r--@ 1 simon-pierreboucher  staff  9705 oct.  30 00:27 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher  staff  1896 oct.  30 00:28 claims.json\n-rw-r--r--@ 1 simon-pierreboucher  staff   126 oct.  30 00:26 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher  staff  5582 oct.  30 00:28 report.md\n-rw-r--r--@ 1 simon-pierreboucher  staff  4816 oct.  30 00:27 results.json\ndrwxr-xr-x  2 simon-pierreboucher  staff    64 oct.  30 00:28 review\ndrwxr-xr-x@ 8 simon-pierreboucher  staff   256 oct.  30 00:28 state\n{\n  \"baseline_verdicts_literal_number_match\": {\n    \"A1\": \"supported\",\n    \"A2\": \"supported\",\n    \"A3\": \"not_supported\",\n    \"A4\": \"not_supported\",\n    \"A5\": \"supported\",\n    \"A6\": \"supported\"\n  },\n  \"computation_details\": {\n    \"A1\": {\n      \"att\": \"-0.431**\",\n      \"pct_computed\": -35.01,\n      \"pct_printed\": \"-35.0%\",\n      \"se\": \"(0.210)\",\n      \"z\": -2.052\n    },\n    \"A2\": {\n      \"att_1km\": \"0.063\",\n      \"se_1km\": \"(0.801)\"\n    },\n    \"A3\": {\n      \"att_3km\": -0.283,\n      \"pct_computed\": -24.65,\n      \"pct_printed\": \"-24.7%\"\n    },\n    \"A4\": {\n      \"att_5km\": -0.406,\n      \"ci95_1.96\": [\n        -0.896,\n        0.084\n      ],\n      \"ci95_2.00\": [\n        -0.906,\n        0.094\n      ],\n      \"pm_1se\": [\n        -0.656,\n        -0.156\n      ],\n      \"se_5km\": 0.25,\n      \"stars\": \"none\"\n    },\n    \"A5\": {\n      \"sex_term_hits\": 0\n    },\n    \"A6\": {\n      \"ate\": -1.99,\n      \"ci\": [\n        -2.24,\n        -1.75\n      ],\n      \"control_mean\": 17.3,\n      \"pct_of_control\": 11.5,\n      \"text_has_11.5\": true\n    }\n  },\n  \"data_issues\": [\n    {\n      \"handling\": \"A1 decided on Table 6 (the cited overall-ATT table); the 5% significance claim holds under either SE (z=-2.05 or -1.98), so the verdict is unaffected; flagged.\",\n      \"issue\": \"DCR Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, while Table 6 prints (0.210) for the same estimate; the A19 note says 640 never-treated regions while A19's body and Tables 6-7 say 608.\"\n    },\n    {\n      \"handling\": \"Not decisive for any assertion; tables used as the authoritative values; flagged.\",\n      \"issue\": \"DCR abstract states fatal hospitalisations fall by 35% and narcotics rise 'by a comparable amount', but Table 7 (5 km: -0.354, +0.390) implies -29.8% and +47.7%, and Section 6.1 says ~30% and 47%.\"\n    },\n    {\n      \"handling\": \"A2 decided on the printed sign of the 1 km Table 7 cell (+0.063), not on the prose.\",\n      \"issue\": \"DCR text describes HIV estimates as 'close to zero' although the 3 km and 5 km cells are -0.239 and -0.340 (insignificant).\"\n    },\n    {\n      \"handling\": \"Not decisive; noted only.\",\n      \"issue\": \"DCR Figure 7 note refers to four diagnostic sub-categories whereas Table 7 and the text list five.\"\n    },\n    {\n      \"handling\": \"Treated as equivalent representations; A6 decided on Table 2 signed values.\",\n      \"issue\": \"RESEA results text prints CIs of reductions as positive magnitudes ([1.75, 2.24]) while Table 2 prints signed bounds [-2.24, -1.75]; the text also contains a rendering defect 'p ¡ 0.001' for p < 0.001.\"\n    }\n  ],\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"locators\": {\n    \"A1\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n    \"A2\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"1 km\\\"\",\n    \"A3\": \"arxiv-2609.27732v1 | Table 6 | row \\\"% change\\\" | column \\\"3 km\\\"\",\n    \"A4\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Hepatitis\\\" | column \\\"5 km\\\"\",\n    \"A5\": \"not reported in arxiv-2609.27732v1\",\n    \"A6\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\"\"\n  },\n  \"n_analysed\": 6,\n  \"n_input_rows\": {\n    \"README.md\": 14,\n    \"arxiv-2606.14621v1.txt\": 58,\n    \"arxiv-2609.27732v1.txt\": 134,\n    \"assertions.json\": 6\n  },\n  \"n_not_assessable\": 1,\n  \"n_not_supported\": 3,\n  \"n_supported\": 2,\n  \"random_seed\": null,\n  \"reasons\": {\n    \"A1\": \"Table 6 prints the 5 km ATT as -0.431** with SE (0.210) (** = p<0.05) and a % change of -35.0%, which equals 100(exp(-0.431)-1) = -35.0%, so sign, magnitude, SE, significance and conversion all match.\",\n    \"A2\": \"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the assertion's negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\",\n    \"A3\": \"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.6%), not 28.3%, so the magnitude is wrong (log points were read as percent); the estimate is also insignificant (SE 0.279).\",\n    \"A4\": \"Table 7 prints the 5 km hepatitis ATT as -0.406 with SE (0.250) and no star, giving a 95% CI of [-0.896, 0.084] (±1.96 SE), which includes zero; the asserted [-0.66, -0.16] is only ±1 SE.\",\n    \"A5\": \"The extract reports ATTs only by buffer, diagnostic category and estimator (Tables 6, 7, A19) and contains no estimate by sex, so the men-versus-women comparison can be neither confirmed nor refuted.\",\n    \"A6\": \"Table 2 prints the ATE on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) with control mean 17.3 weeks, and 1.99/17.3 = 11.5%, matching the Section 3 text.\"\n  },\n  \"task_id\": \"F4-conf-02\",\n  \"verdicts\": {\n    \"A1\": \"supported\",\n    \"A2\": \"not_supported\",\n    \"A3\": \"not_supported\",\n    \"A4\": \"not_supported\",\n    \"A5\": \"not_assessable\",\n    \"A6\": \"supported\"\n  }\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.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints a different SE (0.218) for the same estimate; significance holds under both.\"},\n {\"id\":\"C2\",\"text\":\"A2 is not supported: the 1 km HIV ATT in Table 7 is +0.063, not negative.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"A3 is not supported: a -0.283 log-point ATT corresponds to -24.7%, not 28.3%.\",\"results_keys\":[\"verdicts\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"A4 is not supported: the 95% CI for the 5 km hepatitis ATT is about [-0.896, 0.084] and includes zero.\",\"results_keys\":[\"verdicts\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Robust to using 2.0 instead of 1.96.\"},\n {\"id\":\"C5\",\"text\":\"A5 is not assessable: the DCR extract reports no estimates by sex.\",\"results_keys\":[\"verdicts\",\"locators\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Omitted parts of the paper might contain such estimates.\"},\n {\"id\":\"C6\",\"text\":\"A6 is supported by RESEA Table 2 (-1.99 weeks, CI [-2.24,-1.75], 11.5% of 17.3).\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Overall 2 supported, 3 not supported, 1 not assessable; a literal number-matching baseline would wrongly support A2 and A5.\",\"results_keys\":[\"n_supported\",\"n_not_supported\",\"n_not_assessable\",\"baseline_verdicts_literal_number_match\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C8\",\"text\":\"The sources contain internal inconsistencies (A19 vs Table 6 SE, abstract vs Table 7 percentages) that do not change any verdict.\",\"results_keys\":[\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]","is_error":false}]},"uuid":"f3af0948-8a1f-4d01-81a7-965351e42f9b","timestamp":"2026-10-30T04:28:23.248Z","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 72\ndrwxr-xr-x@ 9 simon-pierreboucher  staff   288 oct.  30 00:28 .\ndrwxr-xr-x  7 simon-pierreboucher  staff   224 oct.  30 00:26 ..\n-rw-r--r--@ 1 simon-pierreboucher  staff  9705 oct.  30 00:27 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher  staff  1896 oct.  30 00:28 claims.json\n-rw-r--r--@ 1 simon-pierreboucher  staff   126 oct.  30 00:26 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher  staff  5582 oct.  30 00:28 report.md\n-rw-r--r--@ 1 simon-pierreboucher  staff  4816 oct.  30 00:27 results.json\ndrwxr-xr-x  2 simon-pierreboucher  staff    64 oct.  30 00:28 review\ndrwxr-xr-x@ 8 simon-pierreboucher  staff   256 oct.  30 00:28 state\n{\n  \"baseline_verdicts_literal_number_match\": {\n    \"A1\": \"supported\",\n    \"A2\": \"supported\",\n    \"A3\": \"not_supported\",\n    \"A4\": \"not_supported\",\n    \"A5\": \"supported\",\n    \"A6\": \"supported\"\n  },\n  \"computation_details\": {\n    \"A1\": {\n      \"att\": \"-0.431**\",\n      \"pct_computed\": -35.01,\n      \"pct_printed\": \"-35.0%\",\n      \"se\": \"(0.210)\",\n      \"z\": -2.052\n    },\n    \"A2\": {\n      \"att_1km\": \"0.063\",\n      \"se_1km\": \"(0.801)\"\n    },\n    \"A3\": {\n      \"att_3km\": -0.283,\n      \"pct_computed\": -24.65,\n      \"pct_printed\": \"-24.7%\"\n    },\n    \"A4\": {\n      \"att_5km\": -0.406,\n      \"ci95_1.96\": [\n        -0.896,\n        0.084\n      ],\n      \"ci95_2.00\": [\n        -0.906,\n        0.094\n      ],\n      \"pm_1se\": [\n        -0.656,\n        -0.156\n      ],\n      \"se_5km\": 0.25,\n      \"stars\": \"none\"\n    },\n    \"A5\": {\n      \"sex_term_hits\": 0\n    },\n    \"A6\": {\n      \"ate\": -1.99,\n      \"ci\": [\n        -2.24,\n        -1.75\n      ],\n      \"control_mean\": 17.3,\n      \"pct_of_control\": 11.5,\n      \"text_has_11.5\": true\n    }\n  },\n  \"data_issues\": [\n    {\n      \"handling\": \"A1 decided on Table 6 (the cited overall-ATT table); the 5% significance claim holds under either SE (z=-2.05 or -1.98), so the verdict is unaffected; flagged.\",\n      \"issue\": \"DCR Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, while Table 6 prints (0.210) for the same estimate; the A19 note says 640 never-treated regions while A19's body and Tables 6-7 say 608.\"\n    },\n    {\n      \"handling\": \"Not decisive for any assertion; tables used as the authoritative values; flagged.\",\n      \"issue\": \"DCR abstract states fatal hospitalisations fall by 35% and narcotics rise 'by a comparable amount', but Table 7 (5 km: -0.354, +0.390) implies -29.8% and +47.7%, and Section 6.1 says ~30% and 47%.\"\n    },\n    {\n      \"handling\": \"A2 decided on the printed sign of the 1 km Table 7 cell (+0.063), not on the prose.\",\n      \"issue\": \"DCR text describes HIV estimates as 'close to zero' although the 3 km and 5 km cells are -0.239 and -0.340 (insignificant).\"\n    },\n    {\n      \"handling\": \"Not decisive; noted only.\",\n      \"issue\": \"DCR Figure 7 note refers to four diagnostic sub-categories whereas Table 7 and the text list five.\"\n    },\n    {\n      \"handling\": \"Treated as equivalent representations; A6 decided on Table 2 signed values.\",\n      \"issue\": \"RESEA results text prints CIs of reductions as positive magnitudes ([1.75, 2.24]) while Table 2 prints signed bounds [-2.24, -1.75]; the text also contains a rendering defect 'p ¡ 0.001' for p < 0.001.\"\n    }\n  ],\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"locators\": {\n    \"A1\": \"arxiv-2609.27732v1 | Table 6 | row \\\"ATT\\\" | column \\\"5 km\\\"\",\n    \"A2\": \"arxiv-2609.27732v1 | Table 7 | row \\\"HIV\\\" | column \\\"1 km\\\"\",\n    \"A3\": \"arxiv-2609.27732v1 | Table 6 | row \\\"% change\\\" | column \\\"3 km\\\"\",\n    \"A4\": \"arxiv-2609.27732v1 | Table 7 | row \\\"Hepatitis\\\" | column \\\"5 km\\\"\",\n    \"A5\": \"not reported in arxiv-2609.27732v1\",\n    \"A6\": \"arxiv-2606.14621v1 | Table 2 | row \\\"Weeks on UI\\\" | column \\\"Average treatment effect estimate\\\"\"\n  },\n  \"n_analysed\": 6,\n  \"n_input_rows\": {\n    \"README.md\": 14,\n    \"arxiv-2606.14621v1.txt\": 58,\n    \"arxiv-2609.27732v1.txt\": 134,\n    \"assertions.json\": 6\n  },\n  \"n_not_assessable\": 1,\n  \"n_not_supported\": 3,\n  \"n_supported\": 2,\n  \"random_seed\": null,\n  \"reasons\": {\n    \"A1\": \"Table 6 prints the 5 km ATT as -0.431** with SE (0.210) (** = p<0.05) and a % change of -35.0%, which equals 100(exp(-0.431)-1) = -35.0%, so sign, magnitude, SE, significance and conversion all match.\",\n    \"A2\": \"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the assertion's negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\",\n    \"A3\": \"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.6%), not 28.3%, so the magnitude is wrong (log points were read as percent); the estimate is also insignificant (SE 0.279).\",\n    \"A4\": \"Table 7 prints the 5 km hepatitis ATT as -0.406 with SE (0.250) and no star, giving a 95% CI of [-0.896, 0.084] (±1.96 SE), which includes zero; the asserted [-0.66, -0.16] is only ±1 SE.\",\n    \"A5\": \"The extract reports ATTs only by buffer, diagnostic category and estimator (Tables 6, 7, A19) and contains no estimate by sex, so the men-versus-women comparison can be neither confirmed nor refuted.\",\n    \"A6\": \"Table 2 prints the ATE on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) with control mean 17.3 weeks, and 1.99/17.3 = 11.5%, matching the Section 3 text.\"\n  },\n  \"task_id\": \"F4-conf-02\",\n  \"verdicts\": {\n    \"A1\": \"supported\",\n    \"A2\": \"not_supported\",\n    \"A3\": \"not_supported\",\n    \"A4\": \"not_supported\",\n    \"A5\": \"not_assessable\",\n    \"A6\": \"supported\"\n  }\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.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Table A19 prints a different SE (0.218) for the same estimate; significance holds under both.\"},\n {\"id\":\"C2\",\"text\":\"A2 is not supported: the 1 km HIV ATT in Table 7 is +0.063, not negative.\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"A3 is not supported: a -0.283 log-point ATT corresponds to -24.7%, not 28.3%.\",\"results_keys\":[\"verdicts\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"A4 is not supported: the 95% CI for the 5 km hepatitis ATT is about [-0.896, 0.084] and includes zero.\",\"results_keys\":[\"verdicts\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Robust to using 2.0 instead of 1.96.\"},\n {\"id\":\"C5\",\"text\":\"A5 is not assessable: the DCR extract reports no estimates by sex.\",\"results_keys\":[\"verdicts\",\"locators\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"Omitted parts of the paper might contain such estimates.\"},\n {\"id\":\"C6\",\"text\":\"A6 is supported by RESEA Table 2 (-1.99 weeks, CI [-2.24,-1.75], 11.5% of 17.3).\",\"results_keys\":[\"verdicts\",\"locators\",\"reasons\",\"computation_details\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C7\",\"text\":\"Overall 2 supported, 3 not supported, 1 not assessable; 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{"parentUuid":"0576297c-c885-4801-bb20-c21d9cfbf94b","isSidechain":false,"promptId":"3fb0c257-bcd7-4b25-bc35-5818557e674d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BhrbLvMDG2RjzWaKhBuczs","type":"tool_result","content":"[\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]\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n\nFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\ncorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\nDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n\n### 6.1 Mechanisms\n\nThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\nsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n(Kerr et al., 2017; Hedrich, 2004).\n\nThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n\nA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\nEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\nReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\nWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\nShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\nShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\nShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n\nTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n\nOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n\nThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n\nAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n\n### 3.1 Cost-benefit analysis\n\nWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n\n### 4.1 Methodological lessons (excerpt)\n\n[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\nReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\nWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n\nTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n\nIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . These fixed effects regressions amount to estimating the coefficient in the regression model\n      14 data/README.md\n      58 data/sources/arxiv-2606.14621v1.txt\n     134 data/sources/arxiv-2609.27732v1.txt\n     206 total","is_error":false}]},"uuid":"b8f72fb7-dbde-421d-86f8-6999ee48bb04","timestamp":"2026-10-30T04:28:24.983Z","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]\nSOURCE EXTRACT — arxiv-2609.27732v1\nTitle: Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\nAuthors: Ana Armendariz (University of St. Gallen)\nBibliographic record: arXiv:2609.27732v1 [econ.GN], submitted 23 Sep 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2609.27732v1\nRetrieved: https://arxiv.org/html/2609.27732v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: 22b06050f4ff6e665aedd80561fa4031b1717867ecfeca71d1d2902ee1b09f52)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, author, date and abstract; Section 6 'Results' in full (Figure notes, Table 6, Table 7, Section 6.1 'Mechanisms'); Appendix Table A19 with its note and the paragraph that discusses it.\nOmitted: Sections 1-5 and 7, the other appendix tables and figures, the references.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\n================================================================\n\n# Do Drug Consumption Rooms Reduce Drug-Related Hospitalizations? Evidence from Switzerland\n\nAna Armendariz*\n\nSeptember 23, 2026\n\nThis paper estimates the causal effect of drug consumption room (DCR) openings on drug-related hospitalizations in Switzerland. I exploit the staggered introduction of DCRs across Swiss cities between 1998 and 2022, using individual-level hospital records and a difference-in-differences estimator. DCR openings reduce drug-related hospitalization rates by roughly 35% within 5 km of a facility. The reduction is most pronounced for fatal outcomes, which fall by 35% while narcotics poisoning hospitalizations rise by a comparable amount, consistent with DCRs converting potentially fatal overdoses into non-fatal hospitalizations. Mental and behavioral disorder hospitalizations decline by 32% while estimates for Hepatitis and HIV are imprecise. The findings support the role of DCRs as an effective harm reduction strategy.\n\nKeywords: Drug Consumption Rooms, Harm Reduction, Hospitalizations, Drugs, Public Health, Causal Inference\n\n## 6 Results\n\nThis section reports the effect of DCR openings on the rate of drug-related hospitalizations per 100,000 inhabitants. I begin with the event-study estimates and then present the overall pooled ATT. I then disaggregate the overall outcome into its sub-outcome categories and report the same estimates for each.\n\nFigure 6: Event study estimates by buffer zone, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust estimator of Callaway and Sant’Anna (2021) for regions within 5 km of a DCR opening. The comparison group consists of never-treated regions (608 control regions). The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. The sample includes all staggered-treated MedStat regions within 5 km of a DCR that opened between 1998 and 2022 and all never-treated regions. The estimation uses a \\pm 5-year window relative to DCR opening, with t=-5 and t=+5 representing pooled endpoint bins that absorb all periods beyond the window. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded through out. 95% confidence intervals are based on standard errors clustered at the MedStat region level. Results for the 1 km and 3 km buffer specifications are reported in Appendix Figure A17.\n\nFigure 6 presents the event study estimates for the 5 km buffer specifications. I focus on the 5 km buffer specification, as it includes the largest number of treated regions and yields the most precise estimates. I summarize dynamic effects over a symmetric five year window around DCR openings. This window avoids relying on distant leads and lags that are informed by only a small subset of adopters. Pre-treatment coefficients are small and statistically insignificant, supporting the parallel trends assumption. The post-treatment coefficients are consistently negative, indicating a reduction in drug-related hospitalizations following DCR openings. The estimates of the overall summary of ATTs based on calendar time aggregation of Callaway and Sant’Anna (2021) is around -35% reduction in hospitalizations due to drugs.\n\nTable 6: Overall Average Treatment Effect on the Treated (ATT)\n\n| 1 km | 3 km | 5 km\n\nATT | -0.399 | -0.283 | -0.431**\n\n| (0.412) | (0.279) | (0.210)\n\n% change | -32.9% | -24.7% | -35.0%\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 | 608 | 608\n\nObservations | 16,175 | 16,875 | 17,250\n\nNote: The table reports the overall ATT estimated using the doubly robust Callaway and Sant’Anna (2021) estimator with never-treated controls and a universal base period. The outcome is the log drug-related hospitalization rate per 100,000 inhabitants. Columns differ in the geographic buffer used to define treated Medstat regions. The control group consists of 608 never-treated Medstat regions\nthroughout. Regions near DCRs that opened before 1998\n(always-treated) are excluded. Standard errors clustered at the Medstat region level in\nparentheses. Significance: {}^{***}p<0.01 , {}^{**}p<0.05 ,\n{}^{*}p<0.10 .\n\nTable 6 reports the pooled ATT for the 1 km, 3 km and 5 km buffer specification, aggregating the dynamic treatment effects into a single estimate averaged across all post-treatment periods and cohorts. The 5 km specification, yields an ATT of -0.431\ncorresponding to a 35% reduction in the drug-related hospitalization rate. This estimate is statistically significant at the 5% level. The 3 km and 1 km estimates point in the same direction but are less precise, due to the smaller number of treated regions.\n\nTo further explore the mechanisms through which DCR openings affect drug-related hospitalizations, I disaggregate the main outcome into five diagnostic\nsub-categories: (i) mental and behavioral disorders due to psychoactive substance use, (ii) viral hepatitis, (iii) HIV, (iv) poisoning by narcotics and psychodysleptics, and (v) fatal drug-related hospitalizations. Accidental poisoning is excluded due to the very small number of cases.\n\nFigure 7: Event study: Sub-outcome estimates,\nwithin 5 km, \\pm 5-year window\n\nNote: The figure plots event-study coefficients from the doubly robust Callaway & Sant’Anna (2021) estimator for four diagnostic sub-categories of drug-related hospitalizations. Each panel uses a separate y -axis to accommodate differences in scale across outcomes. The sample includes all staggered-treated MedStat regions within 5 km of a\nDCR that opened between 1998 and 2022. The reference period is a universal base period. Regions near DCRs that opened before 1998 (always-treated) are excluded. 95% confidence intervals are based on standard errors clustered at the MedStat region level.\n\nTable 7: Overall ATT by Outcome Category and Buffer Zone\n\nOutcome | 1 km | 3 km | 5 km\n\nMental/Behavioral | -0.354 | -0.202 | -0.381*\n\n| (0.382) | (0.244) | (0.220)\n\nHepatitis | -0.270 | -0.413 | -0.406\n\n| (0.486) | (0.270) | (0.250)\n\nHIV | 0.063 | -0.239 | -0.340\n\n| (0.801) | (0.337) | (0.294)\n\nNarcotics | 1.186*** | 0.549*** | 0.390**\n\n| (0.288) | (0.187) | (0.165)\n\nDrug-related (fatal) | -1.207*** | -0.290 | -0.354**\n\n| (0.137) | (0.215) | (0.178)\n\nTreated regions | 7 | 35 | 50\n\nControl regions | 608 (never-treated) | |\n\nNote: Each cell reports the overall ATT from\nthe doubly robust Callaway & Sant’Anna (2021) estimator with\nnever-treated controls and a universal base period. Outcomes are\nlog hospitalization rates per 100,000 inhabitants. Sub-outcomes:\nmental and behavioral disorders (F11–F19), viral hepatitis (B15–B19),\nHIV disease (B20–B24), narcotics/psychodysleptics (T40). The 1 km\nestimates are identified from a single treated region per cohort and\nshould be interpreted with caution. Regions near DCRs opened before\n1998 excluded. Standard errors clustered at the Medstat region level.\nSignificance: {}^{***}p<0.01 , {}^{**}p<0.05 , {}^{*}p<0.10 .\n\nFigure 7 and Table 7 repeat the estimation for the five diagnostic categories. From Table 7, the ATT estimates for hospitalizations due to mental and behavioral disorders are consistently negative and marginally significant at the 5 km buffer specification. Moreover, the estimates of hepatitis are also negative and of comparable magnitude but insignificant. However, the event-study path in Figure 7 shows a gradual decline that builds from year 3 onward, consistent with a slow-acting reduction in needle sharing. HIV estimates are insignificant and close to zero. Narcotics poisoning hospitalizations rise after DCR opening and remain positive across all buffers. Lastly, fatal drug-related hospitalizations move in the opposite direction, falling after opening the DCR. The opposing patterns in narcotics and fatal hospitalizations suggest that DCRs convert events that would otherwise be fatal into survivable hospital contacts.\n\n### 6.1 Mechanisms\n\nThe sub-outcome results point to two channels through which DCRs reduce drug-related hospitalizations. The first is overdose supervision. DCRs provide a medically supervised environment where staff can control the dose consumed, perform resuscitation, and call emergency services when an overdose occurs. When supervision prevents a death that would otherwise happen outside a hospital, the consequence is a simultaneous decline in fatal drug-related hospitalizations and a rise in non-fatal poisoning contacts, as events that would have ended in death instead end in a hospital visit. The results are consistent with this prediction. Drug-related fatal hospitalizations fall near DCRs, by approximately 30% at 5 km, while narcotics and psychodysleptics hospitalizations rise by 47% at 5 km. The opposing signs and spatial concentration of these two effects are consistent with a\nsubstitution from out-of-hospital mortality to in-hospital survival. Since I cannot observe overdose events that do not result in a hospital contact, this interpretation is suggestive rather than identified, but it is consistent with the clinical evidence on DCR-based overdose reversals\n(Kerr et al., 2017; Hedrich, 2004).\n\nThe second channel is harm reduction for blood-borne diseases. DCRs distribute sterile injection equipment, which reduces needle sharing and the transmission of viral hepatitis and HIV. Unlike overdose prevention, these effects are expected to emerge slowly, since disease progression and hospitalization take years to follow from initial infection. The event-study path for hepatitis is consistent with this lag. The estimates are close to zero in the first two post-treatment years and become progressively more negative from year 3 onward, reaching approximately 40% below baseline by year 5. HIV estimates are near zero through out the analysis, with wide confidence intervals. A longer panel and additional out-patient data would be needed to determine whether these trends continue and to get more precise estimates.\n\nA third potential channel is referral to treatment and social services. DCRs routinely connect users to addiction treatment, primary care, and housing support, and a sustained reduction in hospitalizations could partly reflect better long-term management of drug dependence. The regional hospitalization panel used in this paper does not contain information on treatment entry or service use, so I cannot separate this channel from the two described above. It remains a plausible additional mechanism that future work with information on treatment take up data could examine.\n\nTable A19: Robustness: Overall ATT Across Estimators — 5 km buffer, \\pm 5-year window\n\n| CS-DR | Sun & Abraham | Synthetic DiD | Stacked DiD\n\nATT | -0.431** | -0.438** | -0.362* | -0.149***\n\n| (0.218) | (0.208) | (0.205) | (0.038)\n\n% change | -35.0% | -35.5% | -30.3% | -13.9%\n\nTreated regions | 50 | | |\n\nControl regions | 608 | | |\n\nObservations | 17,250 | | |\n\nNote: All specifications use the 5 km buffer,\nnever-treated comparison regions (640 regions), and a \\pm 5-year window\nSOURCE EXTRACT — arxiv-2606.14621v1\nTitle: Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\nAuthors: Harrison H. Li; Shanna Pearson-Merkowitz; David Yokum\nBibliographic record: arXiv:2606.14621v1 [econ.GN], submitted 12 Jun 2026 (version 1, the only version at retrieval). Abstract page: https://arxiv.org/abs/2606.14621v1\nRetrieved: https://arxiv.org/html/2606.14621v1 on 2026-10-30T03:23:18Z (SHA-256 of the retrieved HTML: f98fb1b39edd2107051cddf1da34aa8cf19e74e04d60bf9305a88cf966417da1)\nLicense: CC BY 4.0 (license shown on the arXiv abstract page; https://creativecommons.org/licenses/by/4.0/). Reproduced in part with attribution.\nExtract contents: title, authors and abstract; Section 3 'Results' (Table 2 with caption, the results paragraphs, Section 3.1 'Cost-benefit analysis'); from Section 4.1, Table 3 with its caption and the paragraph that discusses it.\nOmitted: Sections 1-2, Table 1, Section 3.2 (heterogeneous effects), the rest of Section 4, the references and the appendix.\nRendering note: converted from the HTML rendering to plain text. Tables are rendered one row per line with \" | \" separating cells; in regression tables the line following a coefficient row holds the standard errors in parentheses, exactly as printed. Mathematical expressions appear as LaTeX source; non-breaking spaces were replaced by ordinary spaces. No numbers were altered.\nEditorial note: Tables 2 and 3 were re-set in a one-row-per-outcome layout because the HTML rendering split each outcome over three rows and detached the p-values from their columns; the p-values and confidence intervals are attached to the column they belong to in the original (first p-value = treatment-control difference, second = average treatment effect).\n================================================================\n\n# Evaluating the Impact of Rhode Island's Self-Sustaining Reemployment Services and Eligibility Assessment (RESEA) Program on Employment Outcomes\n\nHarrison H. Li (Harvey Mudd College), Shanna Pearson-Merkowitz (University of Maryland, College Park), David Yokum (University of North Carolina at Chapel Hill)\n\n###### Abstract\n\nProlonged unemployment carries serious economic, health, and wellbeing costs. With federal support, most U.S. states now operate a Reemployment Services and Eligibility Assessment (RESEA) program to help Unemployment Insurance (UI) claimants return to work faster. We report results from a large (N = 23,549) preregistered randomized controlled trial (RCT) evaluating Rhode Island’s RESEA program from February 2022 to September 2023. We estimate that selection into the program increased annualized wages by $1,153, increased reemployment by 1.5 percentage points, and reduced UI duration by nearly two weeks. The vast majority of these wage and reemployment effects appeared within two quarters of claimants’ first pay dates and persisted through at least the following year, and we estimate that each dollar spent on the program saved the state $2.64. Using causal forests, a machine learning technique for estimating heterogeneous treatment effects (HTE), we also conduct an exploratory analysis to investigate if there are differential effects of selection into the RESEA program. We find that all participants experienced positive wage benefits from RESEA selection, with particularly large effects for older and lower-income workers. Finally, we improve upon prior RESEA evaluations by explicitly controlling for the week of treatment assignment — a methodological refinement absent from several existing RCTs of job-training programs that is important to eliminate confounding bias. We also discuss ways to harvest precision gains from baseline covariate adjustment without introducing large-sample bias.\n\n## 3 Results\n\n[Table 1 (pre-treatment characteristics) omitted from this extract.]\n\n[Table 2 — re-set by the extract editor from the HTML rendering, in which each outcome occupied three table rows; all values are verbatim. Columns: Outcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate]\n\nOutcome | Treatment | Control | Treatment-control difference | Average treatment effect estimate\nWage | $33,073 (SD: 36,749) | $33,043 (SD: 36,231) | $29; p=0.95; 95% CI: [-903, 962] | $1,153; p=0.022; 95% CI: [166, 2,141]\nReemployment | 75.2% | 75.0% | 0.26 pp; p=0.64; 95% CI: [-0.84, 1.37] | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62]\nWeeks on UI | 15.2 weeks (SD: 9.3) | 17.3 weeks (SD: 9.4) | -2.10 weeks; p<0.001; 95% CI: [-2.34, -1.86] | -1.99 weeks; p<0.001; 95% CI: [-2.24, -1.75]\nShort-term wage | $23,776 (SD: 31,948) | $23,897 (SD: 32,293) | -$121; p=0.77; 95% CI: [-941, 700] | $1,195; p=0.007; 95% CI: [326, 2,064]\nShort-term reemployment | 61.4% | 61.6% | -0.18 pp; p=0.77; 95% CI: [-1.43, 1.06] | 1.66 pp; p=0.012; 95% CI: [0.37, 2.96]\nShort-term weeks on UI | 14.2 weeks (SD: 8.4) | 15.9 weeks (SD: 8.3) | -1.70 weeks; p<0.001; 95% CI: [-1.92, -1.49] | -1.77 weeks; p<0.001; 95% CI: [-1.99, -1.55]\n\nTable 2: The average values of our long-term and short-term outcomes for the aggregate treatment and control groups in the study cohort, along with information about the differences in these averages between the treatment and control groups and our average treatment effect estimates. For the average outcomes, standard deviations are given in parentheses for the quantitative outcomes (wage and weeks on UI). For the treatment-control differences and the average treatment effect estimates, we provide two-sided p -values and 95% confidence intervals based on Welch’s two-sample t -test and the methodology described in the main text, respectively. The two rightmost columns illustrate how direct comparisons of outcome averages between the aggregate treatment and control groups can lead to significantly biased causal conclusions.\n\nOverall, we find that RESEA selection led to a statistically significant improvement in all three of the preregistered outcomes. RESEA selection led to a wage outcome that was $1,153 higher annually (95% CI: [$166, $2,141]; p=0.022 ), a 3.5% increase relative to the mean wage outcome of $33,043 in the control group (Table 2). There was also an estimated 1.47 percentage point increase in reemployment (95% CI: [0.32, 2.62]; p=0.013 ) and a 1.99 week reduction in the number of weeks on UI (95% CI: [1.75 weeks, 2.24 weeks]; p<0.001 ) due to RESEA selection. The latter is an 11.5% decrease compared to the mean control group weeks on UI outcome of 17.3 weeks. All three of these results remain statistically significant after correcting for multiple testing using Holm’s method to keep the familywise error rate below 5 percent.\n\nThe results for the short-term outcomes are substantively similar to those for the longer-term outcomes (Table 2). We estimate RESEA selection leads to a short-term wage outcome that is $1,195 higher (95% CI: [$326, $2,064]; p = 0.007) on an annualized basis and a 1.66 percentage point higher rate of short-term reemployment (95% CI: [0.37, 2.96]; p = 0.012). Given that the magnitudes of these causal estimates are very similar to those for the corresponding long-term outcomes, this suggests that the full impact of RESEA selection on wages and reemployment appears quickly by the second quarter after randomization, and persists for at least five quarters. We also find that those selected for RESEA spend 1.77 fewer weeks on UI in the benefit year of their claim (95% CI: [1.55 weeks, 1.99 weeks]; p ¡ 0.001). As this is a bit smaller than the effect size of 1.99 weeks estimated for the long term weeks on UI outcome, which includes an additional 26 weeks beyond the benefit year, we see some directional evidence that RESEA selection may also reduce the time spent on UI in the following year, suggesting that it may be helping claimants find employment that lasts longer, thereby reducing repetitive use of UI benefits.\n\nAs noted earlier, wages and reemployment are artificially zero-inflated due to missing out-of-state wage data. The Rhode Island job market is tightly integrated with those of the neighboring states of Massachusetts and Connecticut; the 2016-2020 5-Year American Community Survey found that nearly 16 percent of Rhode Island workers 16 years and older commuted out of state, mainly to Massachusetts. We note, however, that this missingness should not affect the directional validity of our causal analysis. This is because randomization ensures that this missingness is balanced between the treatment and control groups (within each block), unless RESEA selection itself differentially helps or hinders finding employment out of state or self-employment. To investigate this, we examined the presence of individuals who failed to exhaust their UI benefits in the benefit year of their claim while having $0 in reported RI wages for all of the five complete quarters after randomization. There were 3,325 claimants falling into this group, including 1,618 individuals (13.7%) in the aggregate control group and 1,707 individuals (14.6%) in the aggregate treatment group. After controlling for randomization week, this difference in proportions was not significant ( p = 0.55). The lack of statistical significance on this test with our large sample size along with the fact that the Rhode Island job database focuses overwhelmingly on jobs offered by Rhode Island employers suggests that RESEA selection does not differentially assist in finding out-of-state employment or self-employment.\n\n### 3.1 Cost-benefit analysis\n\nWe find that the decrease in time spent drawing UI benefits due to RESEA selection corresponds to a considerable cost savings to the state from paying out a smaller amount of UI benefits. We estimate that the average individual selected for RESEA during the study period drew $676 less (95% CI: [$563, $789], p<0.001 ) in UI benefits during the same benefit year of their claim than they would have if they were not selected for RESEA. This is a 9.9 percent reduction relative to the control group average of $6,839 in UI benefits drawn during the benefit year of the claim. Multiplying the estimate by the total number of individuals selected for RESEA in the study ( n=11,700 ) suggests that as a result of the program, the state paid out about $7.9 million fewer in UI benefits to claimants randomized during the study period over the same benefit year as their UI claims. This is much higher than the total expenditures of $2.99 million for the RESEA program over the study period, representing $2.64 in savings for the state budget for every $1 spent on the program.\n\n### 4.1 Methodological lessons (excerpt)\n\n[Table 3 — re-set by the extract editor from the HTML rendering; all values are verbatim. Columns: Outcome | Difference in means | Covariate adjusted | Fixed effects regression]\n\nOutcome | Difference in means | Covariate adjusted | Fixed effects regression\nWage | $1,153; p=0.022; 95% CI: [166, 2,141] | $1,137; p=0.014; 95% CI: [228, 2,047] | $1,100; p=0.027; 95% CI: [127, 2,073]\nReemployment | 1.47 pp; p=0.013; 95% CI: [0.32, 2.62] | 1.48 pp; p=0.012; 95% CI: [0.32, 2.63] | 1.49 pp; p=0.011; 95% CI: [0.34, 2.64]\nWeeks on UI | -1.99 wks; p<0.001; 95% CI: [-2.24, -1.75] | -1.93 wks; p<0.001; 95% CI: [-2.18, -1.68] | -1.97 wks; p<0.001; 95% CI: [-2.22, -1.72]\n\nTable 3: A comparison of the ITT estimates, two-sided p -values, and 95% confidence intervals for our primary outcomes from three different methodologies: the preregistered analysis based on eq. (1), the covariate adjusted analysis based on eq. (2), and the fixed effects regression in eq. (3). The leftmost column based on the preregistered analysis duplicates the rightmost column in Table 2 for ease of comparison.\n\nIf we directly compared treatment group and control group outcome averages without accounting for the week of randomization, the results would suggest RESEA selection had no detectable average effect on the wage and reemployment outcomes (column 4, Table 2). However, such an analysis is potentially biased under the experimental design because it does not respect the blocked randomization, which makes the week of randomization a known confounder. For example, weeks with a larger number of UI applicants, which had a higher proportion of control group individuals, tended to have lower-earning applicants (Fig. 7), thereby inflating the average wage outcome in the control group relative to the treatment group. This is critical because out of the thirteen publically available studies of RESEA-related employment assistance programs rated by CLEAR as having high causal evidence, six used block randomization like we do, yet only two of these (Black, Smith, Berger, & Noel., 2002; Klerman et al., 2019) properly adjusted for the block. Three others among these six studies used regression or matching methods in an attempt to control for observed demographic variables but not the block (Poe-Yamagata et al., 2011; Michaelides et al., 2012; Manoli et al., 2018), while the remaining study (Michaelides & Mueser, 2018) fit a linear regression model with the block indicators as fixed effects, as did two other more recent evaluations in Nevada (Michaelides & Mian, 2021; Brigandi et al., 2024) . 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{"parentUuid":"63379f99-bb8c-4b90-bf66-8f7bbff28f21","isSidechain":false,"promptId":"3fb0c257-bcd7-4b25-bc35-5818557e674d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UXCXnGU89fgngAQCArqfX7","type":"tool_result","content":"\"\"\"F4-conf-02: verify six assertions against cited source extracts.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, math, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR, RES = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\n\ndef lines(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\nassertions = json.load(open(os.path.join(D, \"assertions.json\"), encoding=\"utf-8\"))\nsrc = {s: lines(os.path.join(D, \"sources\", s + \".txt\")) for s in (DCR, RES)}\nsrctext = {s: \"\\n\".join(v) for s, v in src.items()}\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\nn_input_rows = {\n    \"assertions.json\": len(assertions),\n    \"README.md\": wc_l(os.path.join(D, \"README.md\")),\n    DCR + \".txt\": wc_l(os.path.join(D, \"sources\", DCR + \".txt\")),\n    RES + \".txt\": wc_l(os.path.join(D, \"sources\", RES + \".txt\")),\n}\n\ndef find(s, prefix, after=None):\n    \"\"\"Return (lineno, line) of first line starting with prefix (after a given line index).\"\"\"\n    start = 0 if after is None else after\n    for i in range(start, len(src[s])):\n        if src[s][i].startswith(prefix):\n            return i, src[s][i]\n    raise ValueError(f\"{prefix!r} not found in {s}\")\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\ndef num(c):\n    return float(re.sub(r\"[^0-9.\\-]\", \"\", c.replace(\"−\", \"-\")))\n\ndef show(tag, s, i):\n    print(f\"  [{tag}] {s} line {i+1}: {src[s][i]}\")\n\npct = lambda b: 100.0 * (math.exp(b) - 1.0)\ncols = {\"1 km\": 1, \"3 km\": 2, \"5 km\": 3}\n\n# ---- parse DCR Table 6 ----\nt6, _ = find(DCR, \"Table 6:\")\ni_att, l_att = find(DCR, \"ATT |\", t6)\ni_se, l_se = find(DCR, \"| (\", i_att)\ni_pc, l_pc = find(DCR, \"% change |\", i_att)\n# ---- parse DCR Table 7 ----\nt7, _ = find(DCR, \"Table 7:\")\ndef t7row(name):\n    i, l = find(DCR, name + \" |\", t7)\n    j, m = find(DCR, \"| (\", i)\n    return i, l, j, m\n\nV, L, R, details = {}, {}, {}, {}\n\n# A1: 5 km overall ATT, SE, 5% significance, 35.0%\nprint(\"A1:\", assertions[0][\"text\"])\nfor i in (i_att, i_se, i_pc): show(\"Table 6\", DCR, i)\natt5 = cells(l_att)[cols[\"5 km\"]]; se5 = cells(l_se)[cols[\"5 km\"]]; pc5 = cells(l_pc)[cols[\"5 km\"]]\nb, se = num(att5), num(se5)\nstars_ok = att5.endswith(\"**\")  # ** => p<0.05\nok = (b == -0.431 and se == 0.210 and stars_ok and pc5 == \"-35.0%\" and abs(pct(b) - (-35.0)) < 0.05)\ndetails[\"A1\"] = {\"att\": att5, \"se\": se5, \"pct_printed\": pc5, \"pct_computed\": round(pct(b), 2), \"z\": round(b / se, 3)}\nV[\"A1\"] = \"supported\" if ok else \"not_supported\"\nL[\"A1\"] = f'{DCR} | Table 6 | row \"ATT\" | column \"5 km\"'\nR[\"A1\"] = (\"Table 6 prints the 5 km ATT as -0.431** with SE (0.210) (** = p<0.05) and a % change of -35.0%, \"\n           \"which equals 100(exp(-0.431)-1) = -35.0%, so sign, magnitude, SE, significance and conversion all match.\")\n\n# A2: HIV 1 km negative?\nprint(\"A2:\", assertions[1][\"text\"])\ni, l, j, m = t7row(\"HIV\"); show(\"Table 7\", DCR, i); show(\"Table 7\", DCR, j)\nhiv1 = cells(l)[cols[\"1 km\"]]\ndetails[\"A2\"] = {\"att_1km\": hiv1, \"se_1km\": cells(m)[cols[\"1 km\"]]}\nV[\"A2\"] = \"supported\" if num(hiv1) == -0.063 else \"not_supported\"\nL[\"A2\"] = f'{DCR} | Table 7 | row \"HIV\" | column \"1 km\"'\nR[\"A2\"] = (\"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the assertion's \"\n           \"negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\")\n\n# A3: 3 km -0.283 => 28.3% reduction?\nprint(\"A3:\", assertions[2][\"text\"])\nfor i in (i_att, i_pc): show(\"Table 6\", DCR, i)\natt3 = num(cells(l_att)[cols[\"3 km\"]]); pc3 = cells(l_pc)[cols[\"3 km\"]]\ndetails[\"A3\"] = {\"att_3km\": att3, \"pct_printed\": pc3, \"pct_computed\": round(pct(att3), 2)}\nV[\"A3\"] = \"supported\" if abs(pct(att3) - (-28.3)) < 0.05 else \"not_supported\"\nL[\"A3\"] = f'{DCR} | Table 6 | row \"% change\" | column \"3 km\"'\nR[\"A3\"] = (\"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.6%), not 28.3%, \"\n           \"so the magnitude is wrong (log points were read as percent); the estimate is also insignificant (SE 0.279).\")\n\n# A4: hepatitis 5 km 95% CI approx [-0.66,-0.16], excludes zero?\nprint(\"A4:\", assertions[3][\"text\"])\ni, l, j, m = t7row(\"Hepatitis\"); show(\"Table 7\", DCR, i); show(\"Table 7\", DCR, j)\nhb, hse = num(cells(l)[cols[\"5 km\"]]), num(cells(m)[cols[\"5 km\"]])\nci196 = [round(hb - 1.96 * hse, 3), round(hb + 1.96 * hse, 3)]\nci200 = [round(hb - 2.0 * hse, 3), round(hb + 2.0 * hse, 3)]\nci1se = [round(hb - hse, 3), round(hb + hse, 3)]\ndetails[\"A4\"] = {\"att_5km\": hb, \"se_5km\": hse, \"ci95_1.96\": ci196, \"ci95_2.00\": ci200, \"pm_1se\": ci1se,\n                 \"stars\": \"none\"}\nexcl = ci196[1] < 0 or ci196[0] > 0\nV[\"A4\"] = \"supported\" if (excl and abs(ci196[0] + 0.66) < 0.02 and abs(ci196[1] + 0.16) < 0.02) else \"not_supported\"\nL[\"A4\"] = f'{DCR} | Table 7 | row \"Hepatitis\" | column \"5 km\"'\nR[\"A4\"] = (f\"Table 7 prints the 5 km hepatitis ATT as -0.406 with SE (0.250) and no star, giving a 95% CI of \"\n           f\"[{ci196[0]}, {ci196[1]}] (±1.96 SE), which includes zero; the asserted [-0.66, -0.16] is only ±1 SE.\")\n\n# A5: men vs women -- search for sex/gender breakdown\nprint(\"A5:\", assertions[4][\"text\"])\npat = re.compile(r\"\\b(men|women|male|female|sex|gender)\\b\", re.I)\nhits = [(k + 1, ln) for k, ln in enumerate(src[DCR]) if pat.search(ln)]\nprint(f\"  sex/gender terms found in {DCR}: {len(hits)}\", hits)\ndetails[\"A5\"] = {\"sex_term_hits\": len(hits)}\nV[\"A5\"] = \"not_assessable\" if not hits else \"REVIEW\"\nL[\"A5\"] = f\"not reported in {DCR}\"\nR[\"A5\"] = (\"The extract reports ATTs only by buffer, diagnostic category and estimator (Tables 6, 7, A19) and contains \"\n           \"no estimate by sex, so the men-versus-women comparison can be neither confirmed nor refuted.\")\n\n# A6: RESEA weeks on UI\nprint(\"A6:\", assertions[5][\"text\"])\nk, row = find(RES, \"Weeks on UI |\"); show(\"Table 2\", RES, k)\nkt, _ = find(RES, \"Overall, we find\"); print(f\"  [Section 3 text] {RES} line {kt+1}: (contains '1.99 week reduction' and '11.5% decrease ... 17.3 weeks')\")\nc = cells(row)\nctrl = num(c[2].split(\"weeks\")[0]); ate = c[4]\nate_val = float(re.search(r\"(-?\\d+\\.\\d+) weeks\", ate).group(1))\nci = [float(x) for x in re.search(r\"\\[(-?\\d+\\.\\d+), (-?\\d+\\.\\d+)\\]\", ate).groups()]\nrel = 100 * abs(ate_val) / ctrl\ndetails[\"A6\"] = {\"ate\": ate_val, \"ci\": ci, \"control_mean\": ctrl, \"pct_of_control\": round(rel, 2),\n                 \"text_has_11.5\": \"11.5% decrease\" in src[RES][kt]}\nok = ate_val == -1.99 and ci == [-2.24, -1.75] and ctrl == 17.3 and abs(rel - 11.5) < 0.05\nV[\"A6\"] = \"supported\" if ok else \"not_supported\"\nL[\"A6\"] = f'{RES} | Table 2 | row \"Weeks on UI\" | column \"Average treatment effect estimate\"'\nR[\"A6\"] = (\"Table 2 prints the ATE on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) with control mean 17.3 weeks, \"\n           \"and 1.99/17.3 = 11.5%, matching the Section 3 text.\")\n\n# ---- naive baseline: 'supported' iff every number in the assertion appears literally in the cited extract ----\ndef nums_in(t):\n    return re.findall(r\"\\d+\\.\\d+|\\d+\", t.replace(\"−\", \"-\"))\nbaseline = {}\nfor a in assertions:\n    ns = [n for n in nums_in(a[\"text\"]) if n not in (\"1\", \"3\", \"5\", \"95\")]\n    baseline[a[\"id\"]] = \"supported\" if all(n in srctext[a[\"cites\"]] for n in ns) else \"not_supported\"\n\ndata_issues = [\n    {\"issue\": \"DCR Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, while Table 6 prints (0.210) for the same estimate; the A19 note says 640 never-treated regions while A19's body and Tables 6-7 say 608.\",\n     \"handling\": \"A1 decided on Table 6 (the cited overall-ATT table); the 5% significance claim holds under either SE (z=-2.05 or -1.98), so the verdict is unaffected; flagged.\"},\n    {\"issue\": \"DCR abstract states fatal hospitalisations fall by 35% and narcotics rise 'by a comparable amount', but Table 7 (5 km: -0.354, +0.390) implies -29.8% and +47.7%, and Section 6.1 says ~30% and 47%.\",\n     \"handling\": \"Not decisive for any assertion; tables used as the authoritative values; flagged.\"},\n    {\"issue\": \"DCR text describes HIV estimates as 'close to zero' although the 3 km and 5 km cells are -0.239 and -0.340 (insignificant).\",\n     \"handling\": \"A2 decided on the printed sign of the 1 km Table 7 cell (+0.063), not on the prose.\"},\n    {\"issue\": \"DCR Figure 7 note refers to four diagnostic sub-categories whereas Table 7 and the text list five.\",\n     \"handling\": \"Not decisive; noted only.\"},\n    {\"issue\": \"RESEA results text prints CIs of reductions as positive magnitudes ([1.75, 2.24]) while Table 2 prints signed bounds [-2.24, -1.75]; the text also contains a rendering defect 'p ¡ 0.001' for p < 0.001.\",\n     \"handling\": \"Treated as equivalent representations; A6 decided on Table 2 signed values.\"},\n]\n\ncounts = {v: sum(1 for x in V.values() if x == v) for v in (\"supported\", \"not_supported\", \"not_assessable\")}\nresults = {\n    \"task_id\": \"F4-conf-02\",\n    \"verdicts\": V, \"locators\": L, \"reasons\": R,\n    \"n_supported\": counts[\"supported\"], \"n_not_supported\": counts[\"not_supported\"],\n    \"n_not_assessable\": counts[\"not_assessable\"],\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(V),\n    \"random_seed\": None,\n    \"baseline_verdicts_literal_number_match\": baseline,\n    \"computation_details\": details,\n}\nassert sum(counts.values()) == 6 and \"REVIEW\" not in V.values()\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False, sort_keys=True)\nprint(\"\\nVerdicts:\", V)\nprint(\"Counts:\", counts)\nprint(\"Baseline (literal number match):\", baseline)\n# F4-conf-02: Do the cited sources support the six assertions?\n\n## Question\nA literature review makes six assertions (A1–A6). Each one cites one of two arXiv extracts: Armendariz (2026), arXiv:2609.27732v1, on drug consumption rooms (DCRs), and Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, on RESEA. For each assertion I decide, using only the cited extract, whether it is supported, not_supported or not_assessable. Each verdict comes with a locator and a reason (`verdicts`, `locators`, `reasons`).\n\n## Data and exclusions\nI read four files under `data/`. `n_input_rows` gives their sizes: assertions.json has 6 items, README.md 14 lines, the DCR extract 134 lines and the RESEA extract 58 lines. The SHA-256 hashes match the README (see `out/state/data_audit.md`). No assertion was excluded (`out/exclusions.csv`: 6 → 6), so `n_analysed` = 6. Both extracts contain internal inconsistencies, which are listed under `data_issues`. The main one: Table A19 prints a standard error of 0.218 for the 5 km ATT, while Table 6 prints 0.210 for the same estimate. None of the issues changes a verdict.\n\n## Method\n`out/analysis.py` parses the relevant rows of the source tables and prints the source lines it relies on. It then checks every element of each assertion: sign, magnitude, standard error / CI, significance and population. The rules follow TASK.md:\n- a log-point ATT b converts to a percentage change of 100·(exp(b)−1);\n- a 95% CI is b ± 1.96·SE, with 2.0 as the alternative;\n- an assertion that is partly wrong is not_supported.\n\nFor A5, the script also searches the extract for terms about sex or gender. As a naïve baseline, an assertion counts as \"supported\" if every number in it appears somewhere in the extract (`baseline_verdicts_literal_number_match`). The supporting values computed along the way are stored in `computation_details`. No randomness is involved (`random_seed` = null).\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 |\n|---|---|---|---|\n| A1 | supported | arxiv-2609.27732v1, Table 6, row ATT, column 5 km | The table prints −0.431** (0.210) and −35.0%. Converting −0.431 gives −35.01% (`computation_details.A1`). |\n| A2 | not_supported | arxiv-2609.27732v1, Table 7, row HIV, column 1 km | The printed ATT is +0.063 (SE 0.801), so the sign is wrong and \"HIV hospitalisations fall\" is contradicted. |\n| A3 | not_supported | arxiv-2609.27732v1, Table 6, row % change, column 3 km | −0.283 log points is −24.7% (the table prints −24.7%; the conversion gives −24.65%), not 28.3% (`computation_details.A3`). |\n| A4 | not_supported | arxiv-2609.27732v1, Table 7, row Hepatitis, column 5 km | −0.406 ± 1.96·0.250 gives [−0.896, 0.084], which includes 0. The asserted interval is ±1 SE (`computation_details.A4`). |\n| A5 | not_assessable | not reported in arxiv-2609.27732v1 | The extract has no estimates by sex: searching for sex or gender terms finds 0 matches (`computation_details.A5`). |\n| A6 | supported | arxiv-2606.14621v1, Table 2, row Weeks on UI, column ATE | −1.99 weeks, 95% CI [−2.24, −1.75], control mean 17.3, and 1.99/17.3 = 11.50% (`computation_details.A6`). |\n\nThe baseline disagrees with these verdicts on A2 and A5. On A2 it ignores the sign. On A5 the assertion has no numbers to check, so the baseline calls it \"supported\" (`baseline_verdicts_literal_number_match`). This is why every element has to be checked, not just the numbers.\n\n## Uncertainty\nThe verdicts are categorical, so there is no sampling uncertainty in them. Three verdicts depend on a convention:\n- **A1, critical value:** z = −2.05 with SE 0.210, or −1.98 with A19's SE of 0.218. Both are below −1.96 (p ≈ 0.040 and 0.048), and both tables print **, so 5% significance holds under either SE. With a critical value of 2.0 and SE 0.218, the bare z-ratio (−1.98) would just miss. However, the printed stars, which are the source's own significance statement, decide the significance element, so the verdict stays supported.\n- **A1 and A3, log-to-percent conversion:** both conversions agree with the printed % change row. A3 fails under any reading, because 28.3% is simply the log coefficient multiplied by 100. Even the crude approximation %≈100·b gives −28.3% only by treating log points as percent, and the source explicitly prints −24.7%.\n- **A4, critical value:** the CI contains zero under both 1.96 and 2.0. Only a ±1 SE band reproduces [−0.66, −0.16], and that is not a 95% interval. Table 7 also prints no star (p ≥ 0.10). The verdict is robust.\n\nA2, A5 and A6 involve no convention.\n\n## Limitations and open issues\n- Only the extracts were used, as the rules require. The sex comparison in A5 might appear in the parts of the paper that were omitted, which is why it is not_assessable rather than not_supported.\n- The extracts disagree with themselves in places (`data_issues`):\n  - Table A19 and Table 6 print different SEs (0.218 vs 0.210), and the number of control regions is given as both 640 and 608.\n  - The abstract says \"35%\" for fatal hospitalisations, while Table 7 implies −29.8% (`computation_details` does not cover this; it is documented in `out/state/data_audit.md`).\n  - The prose calls the HIV estimates \"close to zero\" although the 3 km and 5 km cells are −0.239 and −0.340.\n  \n  I used the tables as authoritative. None of these issues changes a verdict.\n- `feasibility` = complete; `feasibility_reason` = null.\nstep,rule,n_before,n_after,n_removed,rationale\n1,all six assertions retained (no exclusion),6,6,0,every assertion is assessed\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nrc=0\n  [Section 3 text] arxiv-2606.14621v1 line 37: (contains '1.99 week reduction' and '11.5% decrease ... 17.3 weeks')\n\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}\nBaseline (literal number match): {'A1': 'supported', 'A2': 'supported', 'A3': 'not_supported', 'A4': 'not_supported', 'A5': 'supported', 'A6': 'supported'}\nSAME\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0086","is_error":false}]},"uuid":"8d64db6c-74c1-45fd-940f-ec18b5c108fe","timestamp":"2026-10-30T04:28:29.692Z","toolUseResult":{"stdout":"\"\"\"F4-conf-02: verify six assertions against cited source extracts.\nReads only data/; writes out/results.json deterministically. No randomness.\"\"\"\nimport json, math, os, re\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nD = os.path.join(ROOT, \"data\")\nDCR, RES = \"arxiv-2609.27732v1\", \"arxiv-2606.14621v1\"\n\ndef lines(p):\n    with open(p, encoding=\"utf-8\") as f:\n        return f.read().split(\"\\n\")\n\nassertions = json.load(open(os.path.join(D, \"assertions.json\"), encoding=\"utf-8\"))\nsrc = {s: lines(os.path.join(D, \"sources\", s + \".txt\")) for s in (DCR, RES)}\nsrctext = {s: \"\\n\".join(v) for s, v in src.items()}\n\ndef wc_l(p):\n    with open(p, \"rb\") as f:\n        return f.read().count(b\"\\n\")\n\nn_input_rows = {\n    \"assertions.json\": len(assertions),\n    \"README.md\": wc_l(os.path.join(D, \"README.md\")),\n    DCR + \".txt\": wc_l(os.path.join(D, \"sources\", DCR + \".txt\")),\n    RES + \".txt\": wc_l(os.path.join(D, \"sources\", RES + \".txt\")),\n}\n\ndef find(s, prefix, after=None):\n    \"\"\"Return (lineno, line) of first line starting with prefix (after a given line index).\"\"\"\n    start = 0 if after is None else after\n    for i in range(start, len(src[s])):\n        if src[s][i].startswith(prefix):\n            return i, src[s][i]\n    raise ValueError(f\"{prefix!r} not found in {s}\")\n\ndef cells(line):\n    return [c.strip() for c in line.split(\"|\")]\n\ndef num(c):\n    return float(re.sub(r\"[^0-9.\\-]\", \"\", c.replace(\"−\", \"-\")))\n\ndef show(tag, s, i):\n    print(f\"  [{tag}] {s} line {i+1}: {src[s][i]}\")\n\npct = lambda b: 100.0 * (math.exp(b) - 1.0)\ncols = {\"1 km\": 1, \"3 km\": 2, \"5 km\": 3}\n\n# ---- parse DCR Table 6 ----\nt6, _ = find(DCR, \"Table 6:\")\ni_att, l_att = find(DCR, \"ATT |\", t6)\ni_se, l_se = find(DCR, \"| (\", i_att)\ni_pc, l_pc = find(DCR, \"% change |\", i_att)\n# ---- parse DCR Table 7 ----\nt7, _ = find(DCR, \"Table 7:\")\ndef t7row(name):\n    i, l = find(DCR, name + \" |\", t7)\n    j, m = find(DCR, \"| (\", i)\n    return i, l, j, m\n\nV, L, R, details = {}, {}, {}, {}\n\n# A1: 5 km overall ATT, SE, 5% significance, 35.0%\nprint(\"A1:\", assertions[0][\"text\"])\nfor i in (i_att, i_se, i_pc): show(\"Table 6\", DCR, i)\natt5 = cells(l_att)[cols[\"5 km\"]]; se5 = cells(l_se)[cols[\"5 km\"]]; pc5 = cells(l_pc)[cols[\"5 km\"]]\nb, se = num(att5), num(se5)\nstars_ok = att5.endswith(\"**\")  # ** => p<0.05\nok = (b == -0.431 and se == 0.210 and stars_ok and pc5 == \"-35.0%\" and abs(pct(b) - (-35.0)) < 0.05)\ndetails[\"A1\"] = {\"att\": att5, \"se\": se5, \"pct_printed\": pc5, \"pct_computed\": round(pct(b), 2), \"z\": round(b / se, 3)}\nV[\"A1\"] = \"supported\" if ok else \"not_supported\"\nL[\"A1\"] = f'{DCR} | Table 6 | row \"ATT\" | column \"5 km\"'\nR[\"A1\"] = (\"Table 6 prints the 5 km ATT as -0.431** with SE (0.210) (** = p<0.05) and a % change of -35.0%, \"\n           \"which equals 100(exp(-0.431)-1) = -35.0%, so sign, magnitude, SE, significance and conversion all match.\")\n\n# A2: HIV 1 km negative?\nprint(\"A2:\", assertions[1][\"text\"])\ni, l, j, m = t7row(\"HIV\"); show(\"Table 7\", DCR, i); show(\"Table 7\", DCR, j)\nhiv1 = cells(l)[cols[\"1 km\"]]\ndetails[\"A2\"] = {\"att_1km\": hiv1, \"se_1km\": cells(m)[cols[\"1 km\"]]}\nV[\"A2\"] = \"supported\" if num(hiv1) == -0.063 else \"not_supported\"\nL[\"A2\"] = f'{DCR} | Table 7 | row \"HIV\" | column \"1 km\"'\nR[\"A2\"] = (\"Table 7 prints the 1 km HIV ATT as +0.063 (SE 0.801), positive and insignificant, so the assertion's \"\n           \"negative sign (-0.063) and the claimed fall in HIV hospitalisations are contradicted.\")\n\n# A3: 3 km -0.283 => 28.3% reduction?\nprint(\"A3:\", assertions[2][\"text\"])\nfor i in (i_att, i_pc): show(\"Table 6\", DCR, i)\natt3 = num(cells(l_att)[cols[\"3 km\"]]); pc3 = cells(l_pc)[cols[\"3 km\"]]\ndetails[\"A3\"] = {\"att_3km\": att3, \"pct_printed\": pc3, \"pct_computed\": round(pct(att3), 2)}\nV[\"A3\"] = \"supported\" if abs(pct(att3) - (-28.3)) < 0.05 else \"not_supported\"\nL[\"A3\"] = f'{DCR} | Table 6 | row \"% change\" | column \"3 km\"'\nR[\"A3\"] = (\"Table 6 converts the 3 km ATT of -0.283 to a -24.7% change (100(exp(-0.283)-1) = -24.6%), not 28.3%, \"\n           \"so the magnitude is wrong (log points were read as percent); the estimate is also insignificant (SE 0.279).\")\n\n# A4: hepatitis 5 km 95% CI approx [-0.66,-0.16], excludes zero?\nprint(\"A4:\", assertions[3][\"text\"])\ni, l, j, m = t7row(\"Hepatitis\"); show(\"Table 7\", DCR, i); show(\"Table 7\", DCR, j)\nhb, hse = num(cells(l)[cols[\"5 km\"]]), num(cells(m)[cols[\"5 km\"]])\nci196 = [round(hb - 1.96 * hse, 3), round(hb + 1.96 * hse, 3)]\nci200 = [round(hb - 2.0 * hse, 3), round(hb + 2.0 * hse, 3)]\nci1se = [round(hb - hse, 3), round(hb + hse, 3)]\ndetails[\"A4\"] = {\"att_5km\": hb, \"se_5km\": hse, \"ci95_1.96\": ci196, \"ci95_2.00\": ci200, \"pm_1se\": ci1se,\n                 \"stars\": \"none\"}\nexcl = ci196[1] < 0 or ci196[0] > 0\nV[\"A4\"] = \"supported\" if (excl and abs(ci196[0] + 0.66) < 0.02 and abs(ci196[1] + 0.16) < 0.02) else \"not_supported\"\nL[\"A4\"] = f'{DCR} | Table 7 | row \"Hepatitis\" | column \"5 km\"'\nR[\"A4\"] = (f\"Table 7 prints the 5 km hepatitis ATT as -0.406 with SE (0.250) and no star, giving a 95% CI of \"\n           f\"[{ci196[0]}, {ci196[1]}] (±1.96 SE), which includes zero; the asserted [-0.66, -0.16] is only ±1 SE.\")\n\n# A5: men vs women -- search for sex/gender breakdown\nprint(\"A5:\", assertions[4][\"text\"])\npat = re.compile(r\"\\b(men|women|male|female|sex|gender)\\b\", re.I)\nhits = [(k + 1, ln) for k, ln in enumerate(src[DCR]) if pat.search(ln)]\nprint(f\"  sex/gender terms found in {DCR}: {len(hits)}\", hits)\ndetails[\"A5\"] = {\"sex_term_hits\": len(hits)}\nV[\"A5\"] = \"not_assessable\" if not hits else \"REVIEW\"\nL[\"A5\"] = f\"not reported in {DCR}\"\nR[\"A5\"] = (\"The extract reports ATTs only by buffer, diagnostic category and estimator (Tables 6, 7, A19) and contains \"\n           \"no estimate by sex, so the men-versus-women comparison can be neither confirmed nor refuted.\")\n\n# A6: RESEA weeks on UI\nprint(\"A6:\", assertions[5][\"text\"])\nk, row = find(RES, \"Weeks on UI |\"); show(\"Table 2\", RES, k)\nkt, _ = find(RES, \"Overall, we find\"); print(f\"  [Section 3 text] {RES} line {kt+1}: (contains '1.99 week reduction' and '11.5% decrease ... 17.3 weeks')\")\nc = cells(row)\nctrl = num(c[2].split(\"weeks\")[0]); ate = c[4]\nate_val = float(re.search(r\"(-?\\d+\\.\\d+) weeks\", ate).group(1))\nci = [float(x) for x in re.search(r\"\\[(-?\\d+\\.\\d+), (-?\\d+\\.\\d+)\\]\", ate).groups()]\nrel = 100 * abs(ate_val) / ctrl\ndetails[\"A6\"] = {\"ate\": ate_val, \"ci\": ci, \"control_mean\": ctrl, \"pct_of_control\": round(rel, 2),\n                 \"text_has_11.5\": \"11.5% decrease\" in src[RES][kt]}\nok = ate_val == -1.99 and ci == [-2.24, -1.75] and ctrl == 17.3 and abs(rel - 11.5) < 0.05\nV[\"A6\"] = \"supported\" if ok else \"not_supported\"\nL[\"A6\"] = f'{RES} | Table 2 | row \"Weeks on UI\" | column \"Average treatment effect estimate\"'\nR[\"A6\"] = (\"Table 2 prints the ATE on weeks on UI as -1.99 weeks (95% CI [-2.24, -1.75], p<0.001) with control mean 17.3 weeks, \"\n           \"and 1.99/17.3 = 11.5%, matching the Section 3 text.\")\n\n# ---- naive baseline: 'supported' iff every number in the assertion appears literally in the cited extract ----\ndef nums_in(t):\n    return re.findall(r\"\\d+\\.\\d+|\\d+\", t.replace(\"−\", \"-\"))\nbaseline = {}\nfor a in assertions:\n    ns = [n for n in nums_in(a[\"text\"]) if n not in (\"1\", \"3\", \"5\", \"95\")]\n    baseline[a[\"id\"]] = \"supported\" if all(n in srctext[a[\"cites\"]] for n in ns) else \"not_supported\"\n\ndata_issues = [\n    {\"issue\": \"DCR Table A19 prints SE (0.218) for the CS-DR 5 km ATT -0.431, while Table 6 prints (0.210) for the same estimate; the A19 note says 640 never-treated regions while A19's body and Tables 6-7 say 608.\",\n     \"handling\": \"A1 decided on Table 6 (the cited overall-ATT table); the 5% significance claim holds under either SE (z=-2.05 or -1.98), so the verdict is unaffected; flagged.\"},\n    {\"issue\": \"DCR abstract states fatal hospitalisations fall by 35% and narcotics rise 'by a comparable amount', but Table 7 (5 km: -0.354, +0.390) implies -29.8% and +47.7%, and Section 6.1 says ~30% and 47%.\",\n     \"handling\": \"Not decisive for any assertion; tables used as the authoritative values; flagged.\"},\n    {\"issue\": \"DCR text describes HIV estimates as 'close to zero' although the 3 km and 5 km cells are -0.239 and -0.340 (insignificant).\",\n     \"handling\": \"A2 decided on the printed sign of the 1 km Table 7 cell (+0.063), not on the prose.\"},\n    {\"issue\": \"DCR Figure 7 note refers to four diagnostic sub-categories whereas Table 7 and the text list five.\",\n     \"handling\": \"Not decisive; noted only.\"},\n    {\"issue\": \"RESEA results text prints CIs of reductions as positive magnitudes ([1.75, 2.24]) while Table 2 prints signed bounds [-2.24, -1.75]; the text also contains a rendering defect 'p ¡ 0.001' for p < 0.001.\",\n     \"handling\": \"Treated as equivalent representations; A6 decided on Table 2 signed values.\"},\n]\n\ncounts = {v: sum(1 for x in V.values() if x == v) for v in (\"supported\", \"not_supported\", \"not_assessable\")}\nresults = {\n    \"task_id\": \"F4-conf-02\",\n    \"verdicts\": V, \"locators\": L, \"reasons\": R,\n    \"n_supported\": counts[\"supported\"], \"n_not_supported\": counts[\"not_supported\"],\n    \"n_not_assessable\": counts[\"not_assessable\"],\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": len(V),\n    \"random_seed\": None,\n    \"baseline_verdicts_literal_number_match\": baseline,\n    \"computation_details\": details,\n}\nassert sum(counts.values()) == 6 and \"REVIEW\" not in V.values()\nos.makedirs(os.path.join(ROOT, \"out\"), exist_ok=True)\nwith open(os.path.join(ROOT, \"out\", \"results.json\"), \"w\", encoding=\"utf-8\") as f:\n    json.dump(results, f, indent=2, ensure_ascii=False, sort_keys=True)\nprint(\"\\nVerdicts:\", V)\nprint(\"Counts:\", counts)\nprint(\"Baseline (literal number match):\", baseline)\n# F4-conf-02: Do the cited sources support the six assertions?\n\n## Question\nA literature review makes six assertions (A1–A6). Each one cites one of two arXiv extracts: Armendariz (2026), arXiv:2609.27732v1, on drug consumption rooms (DCRs), and Li, Pearson-Merkowitz & Yokum (2026), arXiv:2606.14621v1, on RESEA. For each assertion I decide, using only the cited extract, whether it is supported, not_supported or not_assessable. Each verdict comes with a locator and a reason (`verdicts`, `locators`, `reasons`).\n\n## Data and exclusions\nI read four files under `data/`. `n_input_rows` gives their sizes: assertions.json has 6 items, README.md 14 lines, the DCR extract 134 lines and the RESEA extract 58 lines. The SHA-256 hashes match the README (see `out/state/data_audit.md`). No assertion was excluded (`out/exclusions.csv`: 6 → 6), so `n_analysed` = 6. Both extracts contain internal inconsistencies, which are listed under `data_issues`. The main one: Table A19 prints a standard error of 0.218 for the 5 km ATT, while Table 6 prints 0.210 for the same estimate. None of the issues changes a verdict.\n\n## Method\n`out/analysis.py` parses the relevant rows of the source tables and prints the source lines it relies on. It then checks every element of each assertion: sign, magnitude, standard error / CI, significance and population. The rules follow TASK.md:\n- a log-point ATT b converts to a percentage change of 100·(exp(b)−1);\n- a 95% CI is b ± 1.96·SE, with 2.0 as the alternative;\n- an assertion that is partly wrong is not_supported.\n\nFor A5, the script also searches the extract for terms about sex or gender. As a naïve baseline, an assertion counts as \"supported\" if every number in it appears somewhere in the extract (`baseline_verdicts_literal_number_match`). The supporting values computed along the way are stored in `computation_details`. No randomness is involved (`random_seed` = null).\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 |\n|---|---|---|---|\n| A1 | supported | arxiv-2609.27732v1, Table 6, row ATT, column 5 km | The table prints −0.431** (0.210) and −35.0%. Converting −0.431 gives −35.01% (`computation_details.A1`). |\n| A2 | not_supported | arxiv-2609.27732v1, Table 7, row HIV, column 1 km | The printed ATT is +0.063 (SE 0.801), so the sign is wrong and \"HIV hospitalisations fall\" is contradicted. |\n| A3 | not_supported | arxiv-2609.27732v1, Table 6, row % change, column 3 km | −0.283 log points is −24.7% (the table prints −24.7%; the conversion gives −24.65%), not 28.3% (`computation_details.A3`). |\n| A4 | not_supported | arxiv-2609.27732v1, Table 7, row Hepatitis, column 5 km | −0.406 ± 1.96·0.250 gives [−0.896, 0.084], which includes 0. The asserted interval is ±1 SE (`computation_details.A4`). |\n| A5 | not_assessable | not reported in arxiv-2609.27732v1 | The extract has no estimates by sex: searching for sex or gender terms finds 0 matches (`computation_details.A5`). |\n| A6 | supported | arxiv-2606.14621v1, Table 2, row Weeks on UI, column ATE | −1.99 weeks, 95% CI [−2.24, −1.75], control mean 17.3, and 1.99/17.3 = 11.50% (`computation_details.A6`). |\n\nThe baseline disagrees with these verdicts on A2 and A5. On A2 it ignores the sign. On A5 the assertion has no numbers to check, so the baseline calls it \"supported\" (`baseline_verdicts_literal_number_match`). This is why every element has to be checked, not just the numbers.\n\n## Uncertainty\nThe verdicts are categorical, so there is no sampling uncertainty in them. Three verdicts depend on a convention:\n- **A1, critical value:** z = −2.05 with SE 0.210, or −1.98 with A19's SE of 0.218. Both are below −1.96 (p ≈ 0.040 and 0.048), and both tables print **, so 5% significance holds under either SE. With a critical value of 2.0 and SE 0.218, the bare z-ratio (−1.98) would just miss. However, the printed stars, which are the source's own significance statement, decide the significance element, so the verdict stays supported.\n- **A1 and A3, log-to-percent conversion:** both conversions agree with the printed % change row. A3 fails under any reading, because 28.3% is simply the log coefficient multiplied by 100. Even the crude approximation %≈100·b gives −28.3% only by treating log points as percent, and the source explicitly prints −24.7%.\n- **A4, critical value:** the CI contains zero under both 1.96 and 2.0. Only a ±1 SE band reproduces [−0.66, −0.16], and that is not a 95% interval. Table 7 also prints no star (p ≥ 0.10). The verdict is robust.\n\nA2, A5 and A6 involve no convention.\n\n## Limitations and open issues\n- Only the extracts were used, as the rules require. The sex comparison in A5 might appear in the parts of the paper that were omitted, which is why it is not_assessable rather than not_supported.\n- The extracts disagree with themselves in places (`data_issues`):\n  - Table A19 and Table 6 print different SEs (0.218 vs 0.210), and the number of control regions is given as both 640 and 608.\n  - The abstract says \"35%\" for fatal hospitalisations, while Table 7 implies −29.8% (`computation_details` does not cover this; it is documented in `out/state/data_audit.md`).\n  - The prose calls the HIV estimates \"close to zero\" although the 3 km and 5 km cells are −0.239 and −0.340.\n  \n  I used the tables as authoritative. None of these issues changes a verdict.\n- `feasibility` = complete; `feasibility_reason` = null.\nstep,rule,n_before,n_after,n_removed,rationale\n1,all six assertions retained (no exclusion),6,6,0,every assertion is assessed\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nrc=0\n  [Section 3 text] arxiv-2606.14621v1 line 37: (contains '1.99 week reduction' and '11.5% decrease ... 17.3 weeks')\n\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}\nBaseline (literal number match): {'A1': 'supported', 'A2': 'supported', 'A3': 'not_supported', 'A4': 'not_supported', 'A5': 'supported', 'A6': 'supported'}\nSAME","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0086","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"63379f99-bb8c-4b90-bf66-8f7bbff28f21","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0086","sessionId":"3abdbcf5-9bd9-4a22-985a-0c628a9d9977","version":"2.1.314","gitBranch":"HEAD"}
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Some verdict tests are also stated as literal equalities with the assertion's numbers, e.g. A2 is 'supported' only if the cell equals -0.063. If the source changed, the reason text could disagree with the verdict. This is allowed by TASK.md and has no effect on the current outputs.\",\n      \"suggested_fix\": \"Build the reason strings from the parsed values, or assert that the parsed values equal the ones quoted in the reasons.\"\n    },\n    {\n      \"id\": \"R2\",\n      \"severity\": \"minor\",\n      \"location\": \"out/report.md, Limitations\",\n      \"issue\": \"The report says the abstract-vs-Table 7 inconsistency is documented only in out/state/data_audit.md. In fact it is also the second entry of results.json data_issues. This is a small cross-reference error.\",\n      \"suggested_fix\": \"Point to results.json data_issues instead.\"\n    },\n    {\n      \"id\": \"R3\",\n      \"severity\": \"minor\",\n      \"location\": \"results.json reasons.A3\",\n      \"issue\": \"The reason quotes the computed conversion as -24.6% (exp(-0.283)-1 = -24.647%) while the table prints -24.7%. Both are far from 28.3%, so the verdict is unaffected, but quoting two roundings side by side could confuse a reader.\",\n      \"suggested_fix\": \"Quote -24.65% (2 dp) as computation_details already does, or cite only the printed -24.7%.\"\n    }\n  ],\n  \"summary\": \"The script reran cleanly in a scratch copy, and the regenerated results.json is identical to the submitted one. All required keys are present, the counts (2/3/1) agree with the verdicts, and n_input_rows matches wc -l and the item count. I checked each verdict against the extracts myself. A1 matches Table 6 (-0.431**, SE 0.210, -35.0%). A2 has the wrong sign (Table 7 prints +0.063). A3 reads log points as percent (the table prints -24.7%). In A4, -0.406±1.96·0.250 gives an interval that includes zero, and the quoted interval is only ±1 SE. A5 has no sex breakdown in the extract. A6 matches Table 2 and the Section 3 text. The code reads only data/, and no outside evidence is used. The Uncertainty section correctly covers the critical-value and log-to-percent conventions, and the claims are proportionate. The data issues it reports are real (A19 SE 0.218 vs 0.210, 640 vs 608 control regions, abstract percentages). Only minor presentational issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}
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{"parentUuid":"59c810c2-f3ec-49d9-8f21-289918ac0096","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnqTZTk9eT8QQ3B23fP","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: the deliverables meet `TASK.md`, and I found only three minor issues. The review is saved in `out/review/review.json`.\n\n**Results reproduce.** I ran `out/analysis.py` on a scratch copy and it regenerated a `results.json` identical to the submitted one. Every required key is present, the counts (2 supported / 3 not supported / 1 not assessable) agree with the verdicts, and the line counts in `n_input_rows` match `wc -l`. The code reads only `data/` and prints the source lines it relies on.\n\n**Verdicts check out.** I checked each one against the extracts myself:\n- **A1 – supported.** Table 6 prints −0.431** with SE (0.210) and −35.0%, and converting −0.431 gives the same −35.0%.\n- **A2 – not supported.** Table 7 prints the 1 km HIV estimate as +0.063, so the claimed negative sign is wrong.\n- **A3 – not supported.** −0.283 log points is −24.7% (as Table 6 prints), not 28.3%; the assertion read log points as percent.\n- **A4 – not supported.** −0.406 ± 1.96 × 0.250 gives [−0.896, 0.084], which includes zero. The quoted interval is only ±1 SE.\n- **A5 – not assessable.** The extract contains no breakdown by sex.\n- **A6 – supported.** It matches Table 2 and the Section 3 text (−1.99 weeks, CI [−2.24, −1.75], 1.99/17.3 = 11.5%).\n\n**Report and claims are proportionate.** The Uncertainty section correctly explains which verdicts depend on the critical value or the log-to-percent conversion, and that none would change under the alternatives. The listed data issues are real, for example the 0.218 vs 0.210 standard error and the 640 vs 608 control-region counts.\n\n**Minor issues:**\n- **R1:** The reason sentences in the script are fixed text rather than built from the parsed numbers, and some checks compare against the assertion's exact figures. This is allowed and doesn't affect the current outputs.\n- **R2:** The report says one source inconsistency is recorded only in a working file, but it is also listed in `data_issues` in `results.json`.\n- **R3:** The A3 reason quotes −24.6% (computed) next to −24.7% (printed), which could confuse a reader; the verdict is unaffected."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1697,"cache_read_input_tokens":26123,"output_tokens":817,"output_tokens_details":{"thinking_tokens":5},"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":1697},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnqTJ5e6snHSzXUtzZn","requestRef":"4fbbff25-506c-458e-ba0d-b4ebaadf23c0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4c459972-0fe4-42ee-bfd5-b487f1181d80","timestamp":"2026-10-30T04:28:54.497Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0086","sessionId":"3abdbcf5-9bd9-4a22-985a-0c628a9d9977","version":"2.1.314","gitBranch":"HEAD"}
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