{"id":"W4408107822","doi":"10.1016/j.labeco.2025.102698","title":"Gender differences in reservation wages in search experiments","year":2025,"lang":"en","type":"article","venue":"Labour Economics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Social Sciences and Humanities Research Council","keywords":"Reservation; Labour economics; Economics; Efficiency wage; Wage; Computer science","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":[0.006007122,0.0001610678,0.0006282293,0.0004609303,0.0003930855,0.001412717,0.0004029321,0.0007867691,0.01549885],"category_scores_gemma":[0.03678094,0.0002550662,0.0002301501,0.0004037318,0.0006444317,0.0012273,0.000506797,0.0007198571,0.001410096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003480251,"about_ca_system_score_gemma":0.0003704219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001595305,"about_ca_topic_score_gemma":0.001897067,"domain_scores_codex":[0.9983906,0.00102862,0.0001080793,0.0001499689,0.0001510079,0.0001717413],"domain_scores_gemma":[0.9692304,0.02444303,0.00186751,0.002519242,0.0006141101,0.001325686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.05148759,0.01567699,0.6935491,0.0005606737,0.0006726679,0.0009754051,0.01383111,0.005061212,0.05495524,0.05981426,0.008587459,0.09482829],"study_design_scores_gemma":[0.001125863,0.004462415,0.9240153,0.00009030925,0.0003021742,0.000773197,0.004576866,0.01537701,0.007938057,0.03708283,0.004151863,0.0001042443],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959812,0.0001516279,0.0004396591,0.0001536108,0.00001574492,0.00001606694,0.0001410046,0.000009556115,0.003091396],"genre_scores_gemma":[0.9972504,0.00004365714,0.0001290261,0.00007193933,0.00000823185,0.00001746263,0.00009456653,0.00001415492,0.002370622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01549885,"threshold_uncertainty_score":0.05184883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07614655199313926,"score_gpt":0.2835396763542021,"score_spread":0.2073931243610628,"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."}}