{"id":"W2264437635","doi":"","title":"Genetics and Insurance Discrimination: Comparative Legislative, Regulatory and Policy Developments and Canadian Options","year":2004,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Conflict of Laws and Jurisdiction","field":"Social Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Insurance law; Statute; Underwriting; Insurance policy; Legislature; Genetic testing; Medical underwriting; General insurance; Key person insurance; Casualty insurance; Business; Context (archaeology); Political science; Actuarial science; Law; Income protection insurance; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004810648,0.00007215791,0.00008579253,0.0001281884,0.001073946,0.0001006249,0.00005040254,0.00004803671,0.000003008639],"category_scores_gemma":[0.00001884275,0.00007206171,0.0000109938,0.0001348317,0.0002432002,0.000231818,0.00001279744,0.000307419,7.634557e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008131495,"about_ca_system_score_gemma":0.004119133,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04256526,"about_ca_topic_score_gemma":0.7219439,"domain_scores_codex":[0.9989299,0.00005462383,0.0001115321,0.000110505,0.0001417087,0.0006517833],"domain_scores_gemma":[0.9996326,0.00001438195,0.0000574212,0.00003143237,0.00007202072,0.0001921518],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00000556632,0.00001038458,0.0151532,0.000001784073,0.00003791276,0.00000102484,0.02953353,0.00001078409,0.00003233825,0.9320961,0.000007937431,0.02310943],"study_design_scores_gemma":[0.00082027,0.0001228764,0.7324128,0.00003475183,0.00001845128,0.0001172348,0.01341695,0.0000104242,0.00001912433,0.2201069,0.03271481,0.0002053999],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9808336,0.004922409,0.0002433914,0.005235167,0.0000552249,0.0001052957,0.000003875023,0.000008360881,0.00859263],"genre_scores_gemma":[0.9849821,0.01407402,0.0001743265,0.00006988394,0.0001515894,0.00000211045,0.000001138476,0.000004125932,0.0005406827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7172596,"threshold_uncertainty_score":0.9638104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02145250573009877,"score_gpt":0.3064814366916654,"score_spread":0.2850289309615666,"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."}}