{"id":"W4410908292","doi":"10.31234/osf.io/4m65p_v2","title":"The presence of the opposite sex itself creates a division of labor","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Employment, Labor, and Gender Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Division (mathematics); Division of labour; Labour economics; Economics; Psychology; Demographic economics; Arithmetic; Mathematics; Market economy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01124453,0.0005245716,0.0007570772,0.0006632993,0.000768502,0.001711776,0.0006597023,0.0005991183,0.005930624],"category_scores_gemma":[0.02004389,0.0003246422,0.002165549,0.0007936342,0.001362418,0.001000426,0.001110115,0.0006573112,0.0002643051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889275,"about_ca_system_score_gemma":0.0007866136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001676535,"about_ca_topic_score_gemma":0.002592335,"domain_scores_codex":[0.9929988,0.004386838,0.0005467272,0.001022633,0.0008719491,0.0001729948],"domain_scores_gemma":[0.9794257,0.01360256,0.004615748,0.001636182,0.0004922246,0.0002275324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01063031,0.001731594,0.6695406,0.0187971,0.02633793,0.0007181522,0.004832328,0.001217512,0.008285375,0.01659832,0.002267692,0.2390429],"study_design_scores_gemma":[0.001548657,0.004986168,0.8919621,0.005156213,0.02363284,0.001245266,0.005351638,0.001828389,0.01306102,0.02282096,0.02822944,0.0001773465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9482341,0.02659849,0.01068998,0.0007549839,0.0005853005,0.0004015383,0.000922302,0.00003177764,0.01178156],"genre_scores_gemma":[0.9910766,0.0028806,0.004398848,0.0003408466,0.00008547949,0.0002348827,0.0002694318,0.000009106409,0.0007040916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01124453,"threshold_uncertainty_score":0.05946749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03282816129284161,"score_gpt":0.3447919368267324,"score_spread":0.3119637755338908,"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."}}