{"id":"W4391491490","doi":"10.1515/jqas-2022-0025","title":"Equity, diversity, and inclusion in sports analytics","year":2024,"lang":"en","type":"article","venue":"Journal of Quantitative Analysis in Sports","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Equity (law); Diversity (politics); Inclusion (mineral); Analytics; Workload; Demographics; League; Psychology; Public relations; Political science; Social psychology; Demography; Sociology; Management; Economics; Law; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003833578,0.0001583415,0.000834707,0.003414716,0.0002338476,0.00006989806,0.0002558387,0.00008939767,0.0003117193],"category_scores_gemma":[0.00006730369,0.0001540659,0.0002966432,0.002489127,0.00007169213,0.0005400698,0.003037583,0.0003366031,0.000003691196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001698271,"about_ca_system_score_gemma":0.00003871934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003535197,"about_ca_topic_score_gemma":0.0005311599,"domain_scores_codex":[0.9978677,0.00001305213,0.001318156,0.0002965106,0.0002673937,0.0002372117],"domain_scores_gemma":[0.998922,0.00007752563,0.0006581694,0.0001629019,0.00008282776,0.00009660068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002574621,0.00008560724,0.9554819,0.00004094554,0.0002066385,0.0009479163,0.00437617,0.008157931,0.000001613569,0.02916035,0.00006397957,0.001451135],"study_design_scores_gemma":[0.0002559347,0.00008198294,0.8195823,0.0001565204,0.0002155539,0.00001660562,0.0003849594,0.1018061,0.000002639875,0.07536788,0.001918506,0.0002109912],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859889,0.00771234,0.00227883,0.0003937842,0.0002481821,0.00005119447,0.00001519781,0.000004812824,0.003306776],"genre_scores_gemma":[0.9936478,0.005567315,0.000424945,0.0001030232,0.00004693231,4.099436e-7,0.000004764275,0.00001103602,0.0001937969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1358996,"threshold_uncertainty_score":0.6282626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07438264840495858,"score_gpt":0.3206992120711895,"score_spread":0.2463165636662309,"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."}}