{"id":"W2048095477","doi":"10.1080/14636778.2010.528189","title":"Genetic discrimination in private insurance: global perspectives","year":2010,"lang":"en","type":"article","venue":"New Genetics and Society","topic":"Intellectual Property and Patents","field":"Business, Management and Accounting","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Genetic discrimination; Genetic testing; Population; Politics; Dilemma; Social insurance; State (computer science); Political science; Actuarial science; Public economics; Business; Economics; Law; Medicine; Biology; Genetics; Environmental health","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":[],"consensus_categories":[],"category_scores_codex":[0.00007989196,0.00009654015,0.00008255367,0.00001761345,0.0001013282,0.0001388006,0.00009261782,0.00008228325,0.0000795722],"category_scores_gemma":[0.00002579992,0.00008258604,0.00004613944,0.0001697642,0.00005884757,0.0001326957,0.00007806092,0.0001351993,0.00002658963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001235436,"about_ca_system_score_gemma":0.00001570991,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004251495,"about_ca_topic_score_gemma":0.0002779731,"domain_scores_codex":[0.9994253,0.00000340665,0.000112452,0.000188323,0.0001115864,0.0001589549],"domain_scores_gemma":[0.9997968,0.000006336754,0.00003980013,0.00009203173,0.00005233289,0.00001271396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004689581,0.0002327899,0.7938454,0.0002087144,0.00004690876,0.000004917707,0.008935198,0.0001504861,0.009650243,0.03433739,0.006994694,0.1455464],"study_design_scores_gemma":[0.0005178479,0.00001129728,0.9677561,0.00001290937,0.00001151009,0.000001435065,0.0009219578,0.008400416,0.00006880101,0.008378228,0.01374766,0.0001718611],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929872,0.0003279768,0.0002769949,0.0003380576,0.0002418695,0.0001168869,0.000001621347,0.00001984166,0.005689508],"genre_scores_gemma":[0.9971996,0.0002832138,0.001198606,0.0005606602,0.0005735436,0.000002895733,0.000003476932,0.000008266929,0.0001697593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1739107,"threshold_uncertainty_score":0.3367761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04261346759056526,"score_gpt":0.2325245699060395,"score_spread":0.1899111023154742,"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."}}