{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001023159,0.0003166731,0.0004890602,0.00005608647,0.0002251509,0.0001858098,0.001038586,0.0001627481,0.01515823],"category_scores_gemma":[0.00008950593,0.000294778,0.0000393648,0.0003676479,0.0002817828,0.0003281097,0.002640227,0.0004806513,0.008455527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006887242,"about_ca_system_score_gemma":0.00006183593,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01501034,"about_ca_topic_score_gemma":0.008472342,"domain_scores_codex":[0.9973693,0.00008814463,0.0004823637,0.001036985,0.000488994,0.0005342408],"domain_scores_gemma":[0.9975928,0.00004247029,0.000279636,0.00173654,0.0000051731,0.0003433696],"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":[0.000004967386,0.00004880768,0.0006896778,0.0003756724,0.00001056202,0.00000765467,0.00003723943,5.754609e-8,0.000001580217,0.00002780342,0.995405,0.003391026],"study_design_scores_gemma":[0.0001460601,0.00005807673,0.01358262,0.0001066871,0.000007669721,0.0000227626,0.0001138882,0.000004003076,6.815508e-7,0.00005070902,0.9856462,0.000260589],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001253543,0.00230132,0.000001710336,0.004898481,0.0005670813,0.0002976728,0.9903412,0.00008303775,0.0002558813],"genre_scores_gemma":[0.0002957926,0.0132125,0.00002783371,0.005391785,0.0001006636,0.00001660788,0.980849,0.00001946778,0.00008633182],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01289294,"threshold_uncertainty_score":0.9999504,"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."}}