{"attempt":1,"datasets":{"gonzalez_blame_shifting":{"description_shown":"This dataset comes from a comprehensive archival review of Latin American governments spanning multiple decades. It combines digitized historical records, semi-supervised machine learning to classify calls for ministerial resignation, and survey-based presidential approval estimates to study the corrective effects of ministerial dismissals on presidential popularity. The dataset includes quarterly observations and political, economic, and institutional covariates to analyze blame-shifting dynamics in presidential systems.\n\nVariables:\ncountry: Name of the country corresponding to the quarterly observation\nhoneymoon_1q: Dummy indicating the first quarter of a newly inaugurated presidential term (1 = yes; 0 = no)\nhoneymoon_2q: Dummy indicating the second quarter of a presidential term's honeymoon period (1 = yes; 0 = no)\nhoneymoon_3q: Dummy indicating the third quarter of a presidential term's honeymoon period (1 = yes; 0 = no)\npreelection_1q: Dummy indicating one quarter before a scheduled presidential election (1 = yes; 0 = no)\npreelection_2q: Dummy indicating two quarters before a scheduled presidential election (1 = yes; 0 = no)\ndismissal_tainted: Number of ministers dismissed after receiving calls for resignation\nage: Average age of all cabinet members at the end of the quarter\nnonpartisan: Proportion of ministers in the cabinet who are not affiliated with any political party\ninflation: Quarterly inflation rate (World Bank)\ngdp: Gross domestic product metric (World Bank)\nallhouse: Binary indicator whether president's party holds absolute majority in legislative houses (1 = yes; 0 = no)\ngovfrac: Index measuring probability two government deputies belong to different parties (0-1)\nreelect_last_year: Binary indicating if president could run for re-election in last year of term (1 = yes; 0 = no)\ncoalition: Binary indicating presence of multi-party governing coalition (1 = yes; 0 = no)\nlagged_approval: Prior quarter's smoothed presidential approval estimate\napproval: Smoothed quarterly presidential approval measure (current quarter)\nlagged_rc: Aggregate number of calls for resignation received by any cabinet member in previous quarter\nlagged_dism_tainted: Number of tainted ministers dismissed in previous quarter\nlagged_age: Average age of cabinet members in previous quarter\nlagged_nonpartisan: Proportion of nonpartisan ministers in previous quarter's cabinet\nvisibility: Binary indicating whether calls for resignation concerned high-visibility topics (1 = yes; 0 = no)\nqtr: Unique identifier for each quarterly observation (YYYYqN)","file_sha256":"608c8167bc620dbb0e614d2a37512458559440537c20ad401d25603915e7727b","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"How does dismissing tainted ministers affect presidential approval?","question_sha256":"b47d7d5305678ccaff862cb7616cce228eb1df08b7dfc0296f0de3485884c1ca","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":0,"skills":[],"task_id":"causcibench_real.q113","task_sha256":"5fc2fb7ea6d1405f5c2a62c1eaf956b80287d6ec0ad66a5db28b7be07a9ae8e9","timestamp":"2026-10-15T17:11:24.568796Z"}
{"attempt":1,"attempts":[],"call_id":"call_861dc297","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":["ffcc96e4fce3b3a80f9c71c37acd738c2adbc28f4ab0fcf79ab9ac11a989c039"],"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"1a1c93dc71a4a2f0f12803dc137f0f350cb9c72cd231e87e3642d906d6f0e079","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":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":1,"task_id":"causcibench_real.q113","timestamp":"2026-10-15T17:11:24.568796Z","usage":{"cached":0,"input":0,"output":0,"thoughts":0},"vendor":false}
{"attempt":1,"call_id":"call_a780c155","epoch":1,"event":"guard","numbers_found":[],"retries_left":1,"run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":2,"task_id":"causcibench_real.q113","timestamp":"2026-10-15T17:11:24.570955Z","turn_id":"call_861dc297","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":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":3,"stopped":"answered","task_id":"causcibench_real.q113","timestamp":"2026-10-15T17:11:24.571048Z"}
{"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":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":4,"task_id":"causcibench_real.q113","timestamp":"2026-10-15T17:11:24.571048Z"}
{"attempt":1,"check":{"check_index":1,"detail":"no answer figure: the answer cites no effect of an estimator call","expected":-21.621,"failure_class":"no_estimate_cited","kind":"answer_figure_close","observed":null,"passed":false,"required":true,"source_id":"causcibench_real.gold.q113","tolerance":{"abs_tol":null,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":0.05,"rule":null},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T171122Z-suite-causcibench_real-baseline-4f971b","seq":5,"task_id":"causcibench_real.q113","timestamp":"2026-10-15T17:11:24.571048Z"}