{"attempt":1,"datasets":{"ds_0044":{"description_shown":"| Variable Name  | Description                                                                                 |\n|----------------|---------------------------------------------------------------------------------------------|\n| Unnamed/Index  | Row identifier / index column carried over from the source CSV (sequence number).          |\n| city           | Municipality name and state (lowercase string, e.g., \"akron, oh\").                         |\n| YEAR           | Census year (decennial year) for the observation (numeric).                                |\n| occ            | Occupation class for municipal employment (e.g., \"blue_collar\", \"white_collar\", \"police\").  |\n| name           | Demographic group label combining race and nativity (e.g., \"white_x_native_born\").         |\n| govt           | Proportion (share) of municipal government jobs in this city\u2013year\u2013occupation held by group. |\n| pop            | Proportion (share) of the municipality's total population comprised by the named group.     |\n| treat_assign   | Binary indicator whether a statewide legislative mandate applied to the municipality that decade (0/1). |\n| treat_actual   | Binary indicator whether the municipality had the municipal civil service commission in effect that decade (0/1). |\n| bandwidth      | Numeric distance in residents between the municipality's population and the state cutoff used in assignment rules. |\n\nData Description: This dataset is a municipality-by-decade panel that links full-count U.S. decennial census records (1850\u20131940) on municipal employment and population composition with administrative indicators derived from state statutes and municipal surveys. Each observation identifies a city and census year, an occupation class, and a demographic group, and reports group shares in the municipal population and in the local government workforce; the file also includes binary flags recording whether a state-level mandate applied and whether a municipal civil service commission was in place, plus a numeric distance-from-cutoff measure used for threshold-based comparisons. The underlying sources include full-count census enumerations (industry/occupation and birthplace fields), historical surveys of municipal civil service adoption, and compiled state session-law information assembled to create a harmonized longitudinal dataset on municipal staff composition.","file_sha256":"d5035ee26421d944dd396fa22c1f412e72a32f59388ef130ad486a3c851a813b","frame_sha256":null,"role":"agent","transforms":[]},"ref_iv":{"description_shown":"","file_sha256":"d5035ee26421d944dd396fa22c1f412e72a32f59388ef130ad486a3c851a813b","frame_sha256":null,"role":"reference","transforms":[{"args":{"query":"occ == 'blue_collar' and name == 'white_x_native_born'"},"op":"filter"},{"args":{"columns":["govt","treat_actual","treat_assign","pop","YEAR","city"]},"op":"select"},{"args":{"columns":["govt","treat_actual","treat_assign","pop","YEAR","city"]},"op":"dropna"},{"args":{"column":"YEAR","drop_first":true,"prefix":"YEAR"},"op":"dummies"},{"args":{"column":"city","drop_first":true,"prefix":"city"},"op":"dummies"}]}},"epoch":1,"event":"sample_init","question":"What is the causal effect of municipal civil service reform adoption on the share of blue-collar municipal government jobs held by native-born white workers, estimated for municipalities whose adoption was externally induced (local compliers)?","question_sha256":"00aff9a6acb5300043926f15ca4a01f3e1c8f4d25b4d46aa52b547a8f91e834f","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":0,"skills":[],"task_id":"crb_papers.q074","task_sha256":"c34219b8aa9595071fae1232f2af4919c9aaf06dc623116c1e56d21fd5ce22fc","timestamp":"2026-10-15T17:09:59.379125Z"}
{"attempt":1,"attempts":[],"call_id":"call_28a64bfc","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"rules-1","model_requested":"rules-1","provider":"baseline","request":{"config":{},"contents_refs":[],"messages_refs":["3cd6d0361e36ea0ca3ad9caef752058bb923038e1baab70b1b2b0e4a21e23709"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b8cc5f573355ab714d2aaeac916ed255964cdc6a5a1d6995ddacbea63b178b6d","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Pelas regras deste roteador n\u00e3o identifiquei um desenho que responda \u00e0 pergunta; nenhuma estimativa foi feita.","caveats":[],"figure_refs":[]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"baseline_answer"}]},"response_id":null,"run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":1,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.379125Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_ee2b165e","epoch":1,"event":"guard","numbers_found":[],"retries_left":1,"run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":2,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381272Z","turn_id":"call_28a64bfc","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Pelas regras deste roteador n\u00e3o identifiquei um desenho que responda \u00e0 pergunta; nenhuma estimativa foi feita.","answer_figure":null,"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":[],"run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":3,"stopped":"answered","task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381362Z"}
{"attempt":1,"check":{"check_index":0,"detail":"every number backed","expected":null,"failure_class":null,"kind":"answer_backed","observed":[],"passed":true,"required":true,"source_id":null,"tolerance":null,"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":4,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381362Z"}
{"attempt":1,"check":{"check_index":1,"detail":"no answer figure: the answer cites no effect of an estimator call","expected":[-0.36172254721849234,0.02393113006444131],"failure_class":"no_estimate_cited","kind":"answer_figure_close","observed":null,"passed":false,"required":true,"source_id":null,"tolerance":{"abs_tol":null,"interval":[-0.36172254721849234,0.02393113006444131],"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"interval"},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":5,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381362Z"}
{"attempt":1,"check":{"check_index":2,"detail":"no answer figure: the answer cites no effect of an estimator call","expected":-0.16889570857702552,"failure_class":null,"kind":"answer_figure_close","observed":null,"passed":false,"required":false,"source_id":"crb_papers.q074.gold","tolerance":{"abs_tol":9.838104012319735e-08,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":6,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381362Z"}
{"attempt":1,"check":{"check_index":3,"detail":"no answer figure: the answer cites no effect of an estimator call","expected":0.09838104012319736,"failure_class":null,"kind":"answer_figure_close","observed":null,"passed":false,"required":false,"source_id":"crb_papers.q074.gold","tolerance":{"abs_tol":9.838104012319735e-08,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T170957Z-suite-crb_papers-baseline-dae0c1","seq":7,"task_id":"crb_papers.q074","timestamp":"2026-10-15T17:09:59.381362Z"}
