{"id":"W2060806036","doi":"10.1111/j.1468-0297.2006.01059.x","title":"Advantageous Effects of Regulatory Adverse Selection in the Life Insurance Market","year":2006,"lang":"en","type":"article","venue":"The Economic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":61,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Adverse selection; Selection (genetic algorithm); Asha; Library science; History; Economic history; Political science; Medicine; Economics; Actuarial science; Philosophy; Computer science; Theology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006442139,0.0005605291,0.001237384,0.0008634149,0.001234088,0.003517748,0.0009825666,0.003087296,0.01394391],"category_scores_gemma":[0.02418907,0.0004513902,0.001020499,0.0005260838,0.004166231,0.003089361,0.002124534,0.00312755,0.0005857169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467258,"about_ca_system_score_gemma":0.001381553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002944148,"about_ca_topic_score_gemma":0.002048312,"domain_scores_codex":[0.9971761,0.001390296,0.00007151685,0.0002391111,0.000290651,0.0008322825],"domain_scores_gemma":[0.96911,0.01957511,0.006817258,0.001478596,0.001070406,0.001948744],"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":[0.001113815,0.001473748,0.02626939,0.0001911667,0.0002586781,0.00241068,0.000733137,0.1523736,0.009163492,0.7716957,0.006166492,0.02815014],"study_design_scores_gemma":[0.0007502043,0.001245758,0.02601086,0.00006835067,0.0003828537,0.0007461772,0.001146394,0.320021,0.002024979,0.6431555,0.004206191,0.0002417727],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.908043,0.0006667327,0.0332408,0.004242577,0.0001261786,0.0001015241,0.0002047381,0.0002011135,0.05317323],"genre_scores_gemma":[0.9972368,0.0001645759,0.0006597241,0.0001919524,0.00005840521,0.00001580568,0.00001434427,0.000007578893,0.001650797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01394391,"threshold_uncertainty_score":0.04664701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005400114073405202,"score_gpt":0.1818291631269497,"score_spread":0.1764290490535445,"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."}}