{"id":"W3019681652","doi":"10.26108/p9xw-mv44","title":"Fighting the gap: Does military service reduce the gender wage gap","year":2009,"lang":"en","type":"article","venue":"AcadiaU-DEV","topic":"Defense, Military, and Policy Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wage; Labour economics; Business; Service (business); Gender gap; Economics; Marketing","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":[0.001418643,0.000158911,0.0003656107,0.0004873468,0.0007980661,0.0007273718,0.0005060913,0.000625358,0.01038667],"category_scores_gemma":[0.009410548,0.00006867533,0.0002769072,0.0006327345,0.0007285942,0.0008585684,0.001001563,0.0005236896,0.0009514331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007824349,"about_ca_system_score_gemma":0.003321921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0165866,"about_ca_topic_score_gemma":0.02628452,"domain_scores_codex":[0.9989267,0.0003613395,0.0000220446,0.00005043139,0.0001291872,0.0005103023],"domain_scores_gemma":[0.997556,0.0008051706,0.0005958714,0.0001042411,0.000177881,0.0007608273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001155867,0.001718997,0.5064789,0.0003962447,0.000168098,0.0003544404,0.00624901,0.002201306,0.001275227,0.05592421,0.03252183,0.3915558],"study_design_scores_gemma":[0.0002219813,0.001580187,0.9018071,0.0006168935,0.0001180906,0.0002450202,0.02232955,0.003556243,0.0008611756,0.02350737,0.04513321,0.00002326589],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9412877,0.001770241,0.0006713933,0.02580076,0.0002521265,0.00003005433,0.0003751285,0.00002866459,0.02978394],"genre_scores_gemma":[0.9972894,0.0004444839,0.0001938764,0.0007203872,0.00008726263,0.00001236012,0.00007613577,0.00000472733,0.001171417],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0165866,"threshold_uncertainty_score":0.03474689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08285506496657387,"score_gpt":0.2666092049026898,"score_spread":0.1837541399361159,"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."}}