{"id":"W6891321009","doi":"10.3886/e204701","title":"Data and Code for: Health, Health Insurance, and Inequality","year":2024,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Life expectancy; Inequality; Health insurance; Matching (statistics); Redistribution (election); Distribution (mathematics); Income distribution; Health data","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.009185701,0.001015188,0.001914883,0.000568459,0.0005334797,0.0008643292,0.005397774,0.0004650705,0.00002946555],"category_scores_gemma":[0.001531195,0.001017949,0.00004807842,0.0007144332,0.0005316831,0.001849796,0.01230928,0.001416343,0.0008424792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002905326,"about_ca_system_score_gemma":0.00131479,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005133428,"about_ca_topic_score_gemma":0.03188377,"domain_scores_codex":[0.9914272,0.0003896783,0.001677784,0.00424499,0.0008911553,0.001369231],"domain_scores_gemma":[0.9841208,0.0005858819,0.001301936,0.01302151,0.00009334816,0.0008765091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001205991,0.0001061309,0.0002369975,0.008773997,0.0001892497,0.00001704116,0.00008164322,1.007918e-7,0.000003446421,0.00004894145,0.9860846,0.004337185],"study_design_scores_gemma":[0.001095256,0.0002179216,0.0006208171,0.001372609,0.0001253182,0.0001224646,0.00005729791,0.0003302929,5.702291e-7,0.0003435176,0.9948576,0.0008563452],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003197482,0.02725362,0.00003403741,0.003479311,0.0008554063,0.001979783,0.9657575,0.0003159162,0.000004625444],"genre_scores_gemma":[0.00006380972,0.02445281,0.001839109,0.00411666,0.000681878,0.00007657529,0.9684645,0.0002773703,0.00002726714],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02675034,"threshold_uncertainty_score":0.9999835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1843954621567266,"score_gpt":0.4398664042353885,"score_spread":0.2554709420786619,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}