{"id":"W2157520004","doi":"10.6000/1929-7092.2013.02.30","title":"Compensation Discrimination for Wide Receivers: Applying Quantile Regression to the National Football League","year":2013,"lang":"en","type":"article","venue":"Journal of Reviews on Global Economics","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"League; Football; Quantile regression; Compensation (psychology); Wage; Sports economics; Quantile; Economics; Race (biology); Econometrics; Advertising; Labour economics; Business; Political science; Psychology; Sociology; Law; Social psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.009812722,0.0005376875,0.001131886,0.002061314,0.0006608208,0.001539354,0.001677494,0.001332444,0.006776436],"category_scores_gemma":[0.02588398,0.0002501004,0.001418406,0.003349059,0.0006965383,0.0009137383,0.001202725,0.002870327,0.001137247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008741569,"about_ca_system_score_gemma":0.0007377723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06224541,"about_ca_topic_score_gemma":0.02972673,"domain_scores_codex":[0.9972304,0.001504084,0.0000987119,0.0004804883,0.0003199736,0.0003663561],"domain_scores_gemma":[0.9843295,0.01062894,0.002194353,0.001375825,0.001018794,0.0004525838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006420521,0.0006469114,0.8427678,0.0001535263,0.001645933,0.0006001724,0.001081888,0.03158499,0.0005080693,0.01368221,0.01048579,0.09620063],"study_design_scores_gemma":[0.0001751045,0.000412695,0.6205336,0.0001663252,0.0007815027,0.0002359618,0.002492096,0.3389576,0.0006395006,0.02690372,0.00860159,0.0001001434],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9348893,0.003155782,0.04867965,0.002928698,0.0002862029,0.0001239167,0.001899535,0.000264747,0.007772238],"genre_scores_gemma":[0.9914385,0.0004196563,0.003441193,0.0002827682,0.0001598931,0.00003082771,0.0009979866,0.00003653923,0.003192682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06224541,"threshold_uncertainty_score":0.1237662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09295936122638267,"score_gpt":0.3000173058211908,"score_spread":0.2070579445948082,"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."}}