{"attempt":1,"datasets":{"ds_0068":{"description_shown":"| Variable Name   | Description                                                                                                                             |\n|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------|\n| (index)         | Row identifier from the CSV export; sequential numeric index with no substantive meaning.                                               |\n| pres_change_reg_1 | Change in a state\u2019s Democratic share of the two\u2011party presidential vote from the previous presidential election, demeaned by election year; percentage points. |\n| rv              | Democratic margin in the most recent prior gubernatorial election (Democratic two\u2011party share minus 50); positive values = Democratic win, negative = Republican win; percentage points. |\n\nData Description: \nThis file is a state\u2013year dataset assembled from historical U.S. election returns to examine how party control at the state level relates to subsequent presidential voting patterns. Each row corresponds to a state in a presidential election year and includes the shift in that state\u2019s Democratic two\u2011party presidential vote relative to the previous presidential contest, along with the Democratic margin from the preceding gubernatorial election. The subset shown here focuses on cases where the prior gubernatorial race was close (roughly within \u00b15 percentage points of parity).\n\nElection data were compiled from the Inter\u2011university Consortium for Political and Social Research (ICPSR) archives and official publications of state election authorities, following the paper\u2019s coding conventions: vote shares are recorded as Democratic two\u2011party percentages, changes are computed across presidential cycles, and margins are centered at 50 percent. The broader study spans elections from the late 19th century through 2012; values are reported in percentage points and are suitable for reproducing the specific specification referenced in the paper.\n","file_sha256":"a9d82417ad247286905c5165c217831bc562f522416d97238b2a6f885e27b0d1","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"What is the average effect of the party of the sitting governor on the subsequent change in that party's two-party presidential vote share in the state?","question_sha256":"ac157e3db3009192becda878bfb3f91d37f8afaf8d0a5fc60b79a7218bfc0d69","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":0,"skills":[],"task_id":"crb_papers.q101","task_sha256":"9885c454808285bdbba371dcdd3df4ade85f10fef653714ee85529ec87bc8eaa","timestamp":"2026-10-15T17:10:30.303981Z"}
{"attempt":1,"attempts":[],"call_id":"call_efa5c97f","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["dfb5c213af9bc120491c144e3453bbbc7dba49308284ac14e1caea33019f6fd0"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1addf2b7d1a94715cd2fb81f94fd8c0cf5f604921e976cf2ee1a8670421fb552","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","thoughts":null,"tool_calls":[{"args":{"dataset":"ds_0068","x":["rv"],"y":"pres_change_reg_1"},"llm_name":"infer__ols","tool_id":"infer.ols","vendor_id":"script_1"}]},"response_id":null,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":1,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.303981Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"arguments":{"cluster":null,"controls":[],"dataset":"ds_0068","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":null,"weights":null,"x":["rv"],"y":"pres_change_reg_1"},"attempt":1,"cached":false,"call_id":"call_7ee0b068","card_version":"0.1.0","code_version":"2925aeab2262","dataset_digests":{"ds_0068":"080a987711e187795f5856c35fb499a3360071e7510be26f1b1b2ffbb27a00a3"},"decided_by":"model","duration_ms":2.7802090626209974,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"c647f8c4383d850181aca21ead43aab81a4999a5f9fc63c65621d4c583c570f2","llm_name":"infer__ols","output_digest":"4eb424817991f7bb0f19241f9d77e73be989e6a96499c38a8f98f07c70d07e07","rationale":"Linha de base ing\u00eanua: regress\u00e3o do resultado no tratamento, sem controles.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_7ee0b068","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":566},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.0050474702666511995},{"flag":false,"name":"df_resid","note":"","value":564},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":0.017524641156518195,"ci_level":0.95,"ci_low":-0.09271415848262472,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.037594758663053264,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":566.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de rv na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de pres_change_reg_1.","unit":""},{"formula":"ep = \u221aV_jj","result":0.028062309531866263,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0007874932162622721}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":-0.037594758663053264},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9641790265687296},{"note":"","symbol":"ep","unit":"","value":0.028062309531866263}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 564 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.rv","label":"Coefficient on rv in the regression of pres_change_reg_1","se":0.028062309531866263,"unit":"","value":-0.037594758663053264}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":0.5020639202011546,"ci_low":-0.3716834986997646,"coef":0.06519021075069506,"p":0.7695577909736103,"se":0.22242051439356042,"t":0.2930944159015148,"term":"Intercept"},{"ci_high":0.017524641156518195,"ci_low":-0.09271415848262472,"coef":-0.037594758663053264,"p":0.18088611750205802,"se":0.028062309531866263,"t":-1.339688688857287,"term":"rv"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"7d540dd2df1b743ed2bc85d3ba4022da181a906bab6605645fae56fa380f51b8","result_sha256":"ce77180fed8beeba4efc19407923479207ca482463bd3bb49b76b208a43d30ce","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":2,"synthetic":false,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.306636Z","tool_id":"infer.ols","turn_id":"call_efa5c97f"}
{"attempt":1,"attempts":[],"call_id":"call_e5c3239a","epoch":1,"event":"model","finish_reason":"tool_use","latency_ms":0.0,"model_reported":"naive-ols","model_requested":"naive-ols","provider":"scripted","request":{"config":{},"contents_refs":[],"messages_refs":["dfb5c213af9bc120491c144e3453bbbc7dba49308284ac14e1caea33019f6fd0","8ab70ddc371531f768f75ad0b92eb6cb75553f1335d3a75472ed5e93c396346c","dbdd597c8ee89ec14a3f4412f8a42028580de2bb1407746f8829ce1ceb288301"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1addf2b7d1a94715cd2fb81f94fd8c0cf5f604921e976cf2ee1a8670421fb552","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b"},"response":{"text":"","thoughts":null,"tool_calls":[{"args":{"answer":"Estimativa ing\u00eanua (infer.ols): coef.rv = -0,03759.","caveats":[],"figure_refs":["call_7ee0b068:coef.rv"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"script_2"}]},"response_id":null,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":3,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.309573Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_05c4b419","epoch":1,"event":"guard","numbers_found":["-0,03759"],"retries_left":1,"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":4,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311040Z","turn_id":"call_e5c3239a","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Estimativa ing\u00eanua (infer.ols): coef.rv = -0,03759.","answer_figure":{"call_id":"call_7ee0b068","ci_high":0.017524641156518195,"ci_low":-0.09271415848262472,"formatted":-0.037594758663053264,"key":"coef.rv","ref":"call_7ee0b068:coef.rv","se":0.028062309531866263,"tool_id":"infer.ols","unit":"","value":-0.037594758663053264},"attempt":1,"caveats":[],"epoch":1,"event":"answer","figure_refs":["call_7ee0b068:coef.rv"],"run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":5,"stopped":"answered","task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311225Z"}
{"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":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":6,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311225Z"}
{"attempt":1,"check":{"check_index":1,"detail":"-0.03759475866 in [-7.596293236, 0.7187704398]?","expected":[-7.596293236030442,0.7187704397674333],"failure_class":null,"kind":"answer_figure_close","observed":-0.037594758663053264,"passed":true,"required":true,"source_id":null,"tolerance":{"abs_tol":null,"interval":[-7.596293236030442,0.7187704397674333],"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"interval"},"trace_call_id":"call_7ee0b068","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":7,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311225Z"}
{"attempt":1,"check":{"check_index":2,"detail":"-0.03759475866 vs -3.438761398131504","expected":-3.438761398131504,"failure_class":null,"kind":"answer_figure_close","observed":-0.037594758663053264,"passed":false,"required":false,"source_id":"crb_papers.q101.gold","tolerance":{"abs_tol":2.1211897132137437e-06,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":"call_7ee0b068","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":8,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311225Z"}
{"attempt":1,"check":{"check_index":3,"detail":"0.02806230953 vs 2.121189713213744","expected":2.121189713213744,"failure_class":null,"kind":"answer_figure_close","observed":0.028062309531866263,"passed":false,"required":false,"source_id":"crb_papers.q101.gold","tolerance":{"abs_tol":2.1211897132137437e-06,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":"call_7ee0b068","weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2","seq":9,"task_id":"crb_papers.q101","timestamp":"2026-10-15T17:10:30.311225Z"}
