{"id":"W1965611040","doi":"10.1111/1468-2354.t01-1-00108","title":"A welfare analysis of social security in a dynastic framework*","year":2003,"lang":"en","type":"article","venue":"International Economic Review","topic":"Financial Literacy, Pension, Retirement Analysis","field":"Business, Management and Accounting","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Social security; Earnings; Altruism (biology); Welfare; Economics; Crowding out; Overlapping generations model; Social Welfare; Yield (engineering); Life expectancy; General equilibrium theory; Microeconomics; Demographic economics; Monetary economics; Psychology; Social psychology; Political science; Sociology; Finance; Population","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005688863,0.0001375129,0.0005615158,0.0006271181,0.00004225534,0.00005759734,0.0002861035,0.00005216091,0.01016339],"category_scores_gemma":[0.0004135453,0.0001408571,0.0003831854,0.0007375751,0.00003052931,0.0004319417,0.00006768013,0.0001037932,0.0002392635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001357146,"about_ca_system_score_gemma":0.00002158716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004352345,"about_ca_topic_score_gemma":0.0006974903,"domain_scores_codex":[0.9986932,0.000023415,0.0007161302,0.0002684444,0.0001595904,0.0001392526],"domain_scores_gemma":[0.9991599,0.00004441229,0.0004983453,0.0001689464,0.0001213263,0.000007079172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009992435,0.00008182572,0.3452933,0.0004781854,0.0002736675,0.000004528043,0.00004243517,0.0002005346,0.000001392796,0.6518185,0.00090938,0.0008862013],"study_design_scores_gemma":[0.0004774512,0.000005534705,0.352982,0.001202167,0.002189013,0.000001065409,0.00005703941,0.02553181,0.000002986352,0.01803364,0.5990027,0.0005146103],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9150653,0.005468822,0.0001570027,0.004242338,0.001042584,0.0006188531,0.0000579357,0.00005189217,0.07329519],"genre_scores_gemma":[0.9975175,0.0007579342,0.00009514839,0.00127103,0.000147145,0.00002273451,0.0001110775,0.00001047002,0.00006693914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6337849,"threshold_uncertainty_score":0.9907414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300026924102335,"score_gpt":0.2733633800497586,"score_spread":0.2603631108087353,"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."}}