{"id":"W6947455602","doi":"10.3886/e204701v1","title":"Health, Health Insurance, and Inequality","year":2024,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Life expectancy; Inequality; Redistribution (election); Health insurance; Matching (statistics); Economic inequality; Distribution (mathematics); Income distribution","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":[0.0008965266,0.0006493664,0.0006073548,0.002158808,0.0005357534,0.001642002,0.001395163,0.001178015,0.02605246],"category_scores_gemma":[0.005633889,0.0004038535,0.0006000758,0.005514431,0.0003669324,0.0009549719,0.001669951,0.001396827,0.01239787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288805,"about_ca_system_score_gemma":0.001171533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06774351,"about_ca_topic_score_gemma":0.08125112,"domain_scores_codex":[0.9993134,0.0001667697,0.00007077587,0.0001668994,0.0001529276,0.0001292178],"domain_scores_gemma":[0.9983653,0.000498248,0.0004071925,0.0002779506,0.0002802637,0.0001710906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001294719,0.00006842612,0.03486755,0.0005955039,0.000108627,0.00005633089,0.00007914408,0.002584286,0.00007137087,0.003450261,0.9513935,0.006595511],"study_design_scores_gemma":[0.00043902,0.0000507299,0.1263168,0.0006955168,0.00008207376,0.0002009213,0.0003768292,0.003905594,0.0003057912,0.00600744,0.8615436,0.0000756505],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002113442,0.0002350315,0.0001194487,0.0004007935,0.00002730453,0.000008362333,0.9957997,0.00007373891,0.001222135],"genre_scores_gemma":[0.008920331,0.0001857462,0.0003993131,0.0001981144,0.0000217001,0.00008427913,0.988857,0.00003026145,0.001303296],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06774351,"threshold_uncertainty_score":0.1346984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08103505392566802,"score_gpt":0.3470872934625813,"score_spread":0.2660522395369133,"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."}}