{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204292,0.001328757,0.0009277504,0.00305524,0.0006904533,0.002160283,0.002257463,0.001855869,0.13197],"category_scores_gemma":[0.007925687,0.0007483164,0.0007907294,0.007466387,0.0004591986,0.001255534,0.002050842,0.002007237,0.1131204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001780918,"about_ca_system_score_gemma":0.002543494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06325127,"about_ca_topic_score_gemma":0.074272,"domain_scores_codex":[0.998917,0.0001670056,0.0001574568,0.0002276109,0.0003193429,0.0002115939],"domain_scores_gemma":[0.9963194,0.0008325349,0.0007071503,0.0006369948,0.001114488,0.0003893387],"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":[0.00002917105,0.00001616921,0.001344939,0.0002322283,0.00001055608,0.00001153663,0.00002286712,0.0001922871,0.00003032381,0.0005855524,0.996139,0.001385317],"study_design_scores_gemma":[0.0002954429,0.00001744172,0.01164788,0.0003189659,0.00001603545,0.00004747507,0.0001305426,0.0003762298,0.0001724546,0.001298886,0.9856447,0.00003392214],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00008314242,0.00001746835,0.00003327938,0.00006933724,0.000009921995,0.000009203603,0.9991185,0.00006721781,0.0005920752],"genre_scores_gemma":[0.0005905115,0.00003800219,0.0002467355,0.00007339826,0.000009061328,0.0001234309,0.9974457,0.00006063121,0.001412501],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.13197,"threshold_uncertainty_score":0.4414836,"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."}}