{"id":"W3121438263","doi":"","title":"Political Economy, Stakeholder Voices, and Saliency: Lessons From International Policies Regulating Insurer Use of Genetic Information","year":2019,"lang":"en","type":"article","venue":"","topic":"Human Rights and Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Negotiation; Politics; Genetic discrimination; Stakeholder; Insurance law; Political science; Business; Public economics; Insurance policy; Economics; Law and economics; General insurance; Genetic testing; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001046945,0.0000513205,0.00008180797,0.00006633013,0.00008975756,0.0001529042,0.00008397306,0.00004550511,0.0006417431],"category_scores_gemma":[0.00002949294,0.00004048708,0.00001517811,0.00003639247,0.0001081154,0.000698668,0.00004574067,0.00003414259,0.00002729268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005081183,"about_ca_system_score_gemma":0.0001186026,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007590836,"about_ca_topic_score_gemma":0.001551268,"domain_scores_codex":[0.999361,0.00002664162,0.000217414,0.00007850772,0.0001658441,0.0001506436],"domain_scores_gemma":[0.999626,0.00008947251,0.00007057733,0.00006903489,0.00007822466,0.00006668345],"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":[0.000002227463,0.000009287497,0.07548565,0.00000288802,0.00001488652,6.966828e-8,0.01010881,0.000008244647,0.000009949524,0.9126031,0.000338016,0.001416883],"study_design_scores_gemma":[0.0002662983,0.00001347897,0.7176811,0.00002041582,0.000006387119,3.319247e-7,0.004878533,0.0004156228,0.00008330029,0.03023056,0.2462665,0.0001374943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8836716,0.000005237979,0.000150156,0.00226566,0.0001146831,0.00009323633,0.00001535025,0.00001677127,0.1136674],"genre_scores_gemma":[0.995647,0.000004717378,0.002562366,0.0005152408,0.00005459177,0.00000185328,0.00001280437,0.000002334131,0.001199147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8823726,"threshold_uncertainty_score":0.9990177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06491892900063818,"score_gpt":0.3016006877319012,"score_spread":0.2366817587312631,"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."}}