{"id":"W2739673893","doi":"10.3386/w23636","title":"Hours, Occupations, and Gender Differences in Labor Market Outcomes","year":2017,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Gender, Labor, and Family Dynamics","field":"Social Sciences","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Labour economics; Demographic economics; Economics; Business","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":[],"consensus_categories":[],"category_scores_codex":[0.008362203,0.0001844458,0.000519268,0.00081674,0.0005116625,0.0002164817,0.0007177033,0.0004962586,0.0003471453],"category_scores_gemma":[0.003703463,0.000180535,0.00009338665,0.0001322446,0.0008281789,0.000242222,0.000169233,0.0005756025,0.00001935903],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182784,"about_ca_system_score_gemma":0.009728686,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0184912,"about_ca_topic_score_gemma":0.01553175,"domain_scores_codex":[0.9963753,0.0004436013,0.0005249506,0.0004771367,0.001713027,0.000465997],"domain_scores_gemma":[0.996405,0.001425649,0.0003363099,0.0002991061,0.00139489,0.0001390363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001096535,0.00004786326,0.8985575,0.00005872971,0.00007685507,0.000002399953,0.001030485,0.000003570628,5.265543e-7,0.06846742,0.0311808,0.0005629265],"study_design_scores_gemma":[0.000232717,0.00001465766,0.8661957,0.00003844039,0.000007282714,7.901938e-7,0.001210788,0.00005793591,1.786682e-7,0.1247026,0.00737706,0.0001618217],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1453714,0.001393447,0.00000209591,0.00099094,0.0008327577,0.0005568209,0.0004791171,0.00001412909,0.8503593],"genre_scores_gemma":[0.9498937,0.01452909,0.0001365286,0.00002311187,0.0004848175,0.00006492948,0.0001461423,0.00002130952,0.03470033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.815659,"threshold_uncertainty_score":0.9958853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3907567157579422,"score_gpt":0.5301811496742411,"score_spread":0.1394244339162989,"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."}}