{"id":"W3174901145","doi":"10.2139/ssrn.3849716","title":"The Power of Prediction: Predictive Analytics, Workplace Complements, and Business Performance","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Predictive power; Predictive analytics; Business analytics; Analytics; Business; Power (physics); Business intelligence; Data science; Econometrics; Computer science; Data mining; Business model; Economics; Marketing; Business analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006761894,0.0009202851,0.0007790657,0.001942453,0.001062691,0.006321483,0.001053651,0.001777446,0.00432314],"category_scores_gemma":[0.04614946,0.0002933057,0.0004150734,0.003013737,0.002100606,0.007766744,0.002657311,0.00267702,0.0008606288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094647,"about_ca_system_score_gemma":0.001502017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008327978,"about_ca_topic_score_gemma":0.005838084,"domain_scores_codex":[0.9972441,0.001576309,0.00009865181,0.0003967449,0.0004753417,0.0002089427],"domain_scores_gemma":[0.9544675,0.03711857,0.003122623,0.002058965,0.002189561,0.001042666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001400724,0.001119221,0.4523928,0.0003782192,0.0003596068,0.0003490708,0.003559611,0.03974192,0.000503369,0.07712755,0.01841048,0.4046574],"study_design_scores_gemma":[0.0001109942,0.0005002847,0.1052424,0.0005210425,0.0002799014,0.0001985156,0.00588058,0.2669104,0.001405994,0.6091991,0.009583328,0.0001672729],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8038365,0.009011741,0.06009732,0.05052324,0.0007712731,0.00009001201,0.001259518,0.0005933379,0.07381713],"genre_scores_gemma":[0.9959436,0.0006580537,0.002052626,0.0002589798,0.0002171978,0.00001387424,0.0001351616,0.00001775816,0.0007028389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008327978,"threshold_uncertainty_score":0.03576076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0199206373941025,"score_gpt":0.2470276865929869,"score_spread":0.2271070491988844,"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."}}