{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01817881,0.0003246302,0.0004344555,0.001670452,0.01201564,0.01310447,0.001019673,0.006684022,0.004640991],"category_scores_gemma":[0.01550012,0.000302449,0.0004213377,0.001773772,0.03221874,0.009206294,0.007232329,0.00804566,0.0002135605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01494558,"about_ca_system_score_gemma":0.0123587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03785473,"about_ca_topic_score_gemma":0.04305526,"domain_scores_codex":[0.9914981,0.004833219,0.0001939693,0.0005118419,0.0009966879,0.001966228],"domain_scores_gemma":[0.98237,0.01321176,0.001064572,0.0007001174,0.001483943,0.001169514],"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":[0.00002985857,0.00003416257,0.0027898,0.0000627392,0.00001090317,0.0003371722,0.05905455,0.0002873778,0.0001453668,0.9177033,0.005419566,0.01412533],"study_design_scores_gemma":[0.00005639304,0.00007922867,0.01226896,0.00135592,0.00004927961,0.0003212472,0.1906461,0.001107667,0.001160634,0.4535329,0.3393262,0.00009539226],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1788544,0.0095695,0.004719888,0.2827863,0.0008078174,0.00005726407,0.00006655101,0.00002270304,0.5231157],"genre_scores_gemma":[0.9763829,0.002571637,0.0005375224,0.01436558,0.0002224515,0.00002797967,0.00001905817,0.00002734756,0.005845561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03785473,"threshold_uncertainty_score":0.1084383,"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."}}