{"id":"W2952540627","doi":"10.1017/asb.2016.19","title":"EQUITABLE RETIREMENT INCOME TONTINES: MIXING COHORTS WITHOUT DISCRIMINATING","year":2016,"lang":"en","type":"preprint","venue":"Astin Bulletin","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; University of New South Wales; Macquarie University","keywords":"Pooling; Pension; Economics; Actuarial science; Value (mathematics); Longevity risk; Microeconomics; Public economics; Labour economics; Finance","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005228498,0.0006547892,0.000887047,0.0003229638,0.001279115,0.0005333658,0.001419971,0.0004319035,0.001818313],"category_scores_gemma":[0.0009008421,0.0006025135,0.0003876199,0.0002889859,0.00065091,0.0001063597,0.002658846,0.0007509685,0.0004126742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004719959,"about_ca_system_score_gemma":0.0002260261,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007306383,"about_ca_topic_score_gemma":0.001529249,"domain_scores_codex":[0.9933625,0.0008004148,0.001081137,0.001356487,0.00178907,0.001610315],"domain_scores_gemma":[0.9970261,0.0002990885,0.000914979,0.001094858,0.000380851,0.0002841508],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007537746,0.0003125028,0.8681357,0.001349792,0.0004772133,0.0001380896,0.01001921,0.0001404137,0.00009939951,0.03410311,0.05887301,0.02627621],"study_design_scores_gemma":[0.001540761,0.0001274781,0.1532499,0.007492661,0.0006279519,0.00000293093,0.00743429,0.0001003007,0.000232193,0.02405358,0.8017302,0.003407743],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2791709,0.001475667,0.006582268,0.01827571,0.008149353,0.004382082,0.000135592,0.001202313,0.6806261],"genre_scores_gemma":[0.9787071,0.000343719,0.004672869,0.0003308089,0.001620194,0.0004543783,0.00004078922,0.0001050332,0.01372511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7428572,"threshold_uncertainty_score":0.9996426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02985092650735261,"score_gpt":0.3193995007490674,"score_spread":0.2895485742417148,"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."}}