{"id":"W2114340434","doi":"10.1123/ssj.2014-0069","title":"‘The Datafication of Everything’: Toward a Sociology of Sport and Big Data","year":2015,"lang":"en","type":"article","venue":"Sociology of Sport Journal","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Big data; Premise; Variety (cybernetics); Sociology; Tracking (education); Epistemology; Data science; Computer science; Philosophy; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002953839,0.00009715177,0.0005512758,0.0001202563,0.00008675815,0.000005707426,0.0005261208,0.0001634278,0.00003575619],"category_scores_gemma":[0.00004212745,0.00008140688,0.0000715975,0.00006329608,0.001465366,0.0001520651,0.0001482556,0.0002726012,0.000004448508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002616764,"about_ca_system_score_gemma":0.0001292139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001298264,"about_ca_topic_score_gemma":0.000009028126,"domain_scores_codex":[0.9984886,0.000002746005,0.001057751,0.0002061293,0.00005416833,0.0001906127],"domain_scores_gemma":[0.9975764,0.00001802293,0.001725425,0.0004606488,0.000142807,0.00007666841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004715085,0.00003980741,0.9508892,0.00002098223,0.000115933,0.000002792914,0.002411133,0.00002735999,0.000004598892,0.04399101,0.001777912,0.0006721039],"study_design_scores_gemma":[0.0008989522,0.0002640014,0.8410869,0.00002174583,0.00004463995,0.0001030592,0.003261028,0.001256808,0.00003037952,0.06628525,0.08655994,0.0001873386],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991365,0.006047526,0.0001937315,0.0009287564,0.000642291,0.00005833749,0.00009851874,0.000002777749,0.0006630544],"genre_scores_gemma":[0.9946255,0.004497921,0.0003646726,0.0000617511,0.0002293661,8.517085e-7,0.00008723376,0.000008841679,0.0001238455],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1098023,"threshold_uncertainty_score":0.5399202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1697725381911924,"score_gpt":0.2898104568922053,"score_spread":0.1200379187010129,"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."}}