{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149505,0.0001777484,0.0002465507,0.0005477919,0.0002145054,0.00067165,0.0002740311,0.0003426723,0.00730442],"category_scores_gemma":[0.005485186,0.0001135276,0.0002534143,0.0005751744,0.0003572425,0.0003297256,0.0004772744,0.0003705743,0.0007830692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003238904,"about_ca_system_score_gemma":0.0002878343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005355432,"about_ca_topic_score_gemma":0.007426032,"domain_scores_codex":[0.9997064,0.00008319928,0.0000173549,0.00006001874,0.00006888024,0.00006401964],"domain_scores_gemma":[0.9961885,0.001668634,0.001324274,0.0002710341,0.0001540744,0.0003935872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002148763,0.0001507573,0.979713,0.00002312026,0.00006759931,0.0001259102,0.0003570403,0.003716994,0.0007267555,0.003268157,0.001912168,0.0097237],"study_design_scores_gemma":[0.00000936607,0.00005164773,0.9919482,0.00001132532,0.00001440951,0.0000696244,0.0003030481,0.003846409,0.0001601067,0.002791554,0.0007874802,0.000006838243],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926858,0.0002311625,0.0008437054,0.0002919435,0.00001505355,0.00001009368,0.001442403,0.0000149305,0.004464886],"genre_scores_gemma":[0.9981595,0.00007812289,0.0001465433,0.00003870023,0.0000113593,0.000007424395,0.000615802,0.000003814672,0.0009387858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00730442,"threshold_uncertainty_score":0.02443576,"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."}}