{"id":"W4406143983","doi":"10.2139/ssrn.5085410","title":"Commitment and Nitpicky Behaviors in Insurance","year":2025,"lang":"en","type":"preprint","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":"University of Guelph","funders":"","keywords":"Commit; Monopoly; Commitment device; Welfare; Business; Key person insurance; Insurance fraud; Robustness (evolution); Insurance policy; Principal (computer security); Actuarial science; Auto insurance risk selection; Moral hazard; Bancassurance; Microeconomics; Economics; Incentive; Market economy; Computer science","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":[0.00208465,0.0002116538,0.0004971898,0.0004917146,0.0007173954,0.002062868,0.0006007058,0.001378916,0.01292603],"category_scores_gemma":[0.01726401,0.0003509526,0.0002492634,0.0004470797,0.001211465,0.001980022,0.001250196,0.002410936,0.0005236464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007019828,"about_ca_system_score_gemma":0.0003314224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002889135,"about_ca_topic_score_gemma":0.003379391,"domain_scores_codex":[0.9995042,0.0002028905,0.0000275557,0.00008865652,0.00005771284,0.0001190918],"domain_scores_gemma":[0.9847498,0.009301119,0.002715036,0.0006989173,0.0003044092,0.002230769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002292548,0.001823838,0.4777288,0.0002146935,0.0002777431,0.002237658,0.005150164,0.03675213,0.005195804,0.4070407,0.005003112,0.05628284],"study_design_scores_gemma":[0.0001951139,0.0004782729,0.2529981,0.00006469856,0.00009633689,0.0007800665,0.004604081,0.2365217,0.000760705,0.5016181,0.001775403,0.0001073907],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883425,0.0002598168,0.005815264,0.00107121,0.00001618383,0.00001034414,0.00007423366,0.00002509938,0.004385467],"genre_scores_gemma":[0.9972837,0.00006289968,0.0002432115,0.00004459915,0.00001400794,0.000005043406,0.00002885921,0.000005177202,0.002312387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01292603,"threshold_uncertainty_score":0.04324192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01455557532285252,"score_gpt":0.2338692140354081,"score_spread":0.2193136387125555,"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."}}