{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01085594,0.0003223348,0.0004316535,0.003815955,0.01613822,0.01183177,0.002503791,0.00869123,0.006870851],"category_scores_gemma":[0.01759722,0.0005328689,0.0007677975,0.006369972,0.01677071,0.003467631,0.003529089,0.005517944,0.0001558735],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1531459,"about_ca_system_score_gemma":0.1687184,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9730083,"about_ca_topic_score_gemma":0.9844211,"domain_scores_codex":[0.9883334,0.002021987,0.0003368826,0.0008959281,0.004218658,0.004193307],"domain_scores_gemma":[0.9842858,0.008239809,0.001064351,0.0003818556,0.003931437,0.002096795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005807822,0.00004885478,0.003636498,0.0001364677,0.00001442877,0.0003516347,0.009223402,0.0005904393,0.0003843969,0.9506484,0.009432727,0.02547465],"study_design_scores_gemma":[0.0001951291,0.0001688414,0.09110864,0.001511469,0.0002232352,0.0005927368,0.03824387,0.002327641,0.001928775,0.1003927,0.762934,0.0003730473],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1182284,0.04379969,0.001395202,0.3589234,0.0007914439,0.00009786098,0.000325569,0.00004830473,0.4763901],"genre_scores_gemma":[0.8763046,0.02617565,0.002064393,0.05876549,0.0003753623,0.00007080007,0.0001740678,0.00003608773,0.03603358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1531459,"threshold_uncertainty_score":0.9822307,"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."}}