{"id":"W4406048207","doi":"10.2139/ssrn.5073886","title":"Exploring the life insurance regulations of Canada with a focus on how they mitigate adverse selection","year":2025,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Adverse selection; Business; Government (linguistics); Selection (genetic algorithm); Life insurance; Risk assessment; Actuarial science; Public economics; Environmental resource management; Economics","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":[0.002856491,0.0002720503,0.0004105371,0.001467498,0.004104501,0.005916448,0.001267407,0.002043398,0.006501528],"category_scores_gemma":[0.01138711,0.0001848062,0.0006774828,0.002536644,0.00229209,0.0009668735,0.001190059,0.002273351,0.0002102384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0552523,"about_ca_system_score_gemma":0.1422006,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9883251,"about_ca_topic_score_gemma":0.9951958,"domain_scores_codex":[0.995843,0.0005204906,0.00007389516,0.0002388488,0.001416508,0.00190733],"domain_scores_gemma":[0.9920709,0.002187161,0.001109799,0.0001888031,0.003050538,0.001392886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002731832,0.0003037437,0.1831437,0.0003029757,0.0002841828,0.0009911512,0.004675863,0.01832156,0.001569452,0.6560686,0.07695276,0.05711271],"study_design_scores_gemma":[0.0003144073,0.0003190861,0.5015175,0.0007859685,0.0008713708,0.0002595025,0.02222864,0.01992387,0.002080406,0.08082124,0.3705592,0.0003188041],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6250561,0.01074441,0.004860941,0.09995243,0.0004899519,0.0002434654,0.003224397,0.0001074889,0.2553208],"genre_scores_gemma":[0.9749388,0.00199703,0.001107109,0.005078718,0.00009886101,0.00002336619,0.000246148,0.00001924628,0.0164907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0552523,"threshold_uncertainty_score":0.4008853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01726708059222823,"score_gpt":0.1825410649566033,"score_spread":0.165273984364375,"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."}}