{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009577,0.0001469385,0.0001691413,0.00009047945,0.0005370611,0.0002505178,0.0002983759,0.00004812122,0.0001010042],"category_scores_gemma":[0.0001465611,0.000105281,0.00004602789,0.0007223124,0.0001503889,0.000939828,0.0001912498,0.0006371863,0.00001403874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056489,"about_ca_system_score_gemma":0.000492379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003783145,"about_ca_topic_score_gemma":0.0002828065,"domain_scores_codex":[0.9981631,0.00001527922,0.0003556488,0.0001965176,0.0003429997,0.0009264289],"domain_scores_gemma":[0.9986176,0.00004792983,0.0002875621,0.0002262219,0.000807774,0.0000129343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007458837,0.0003814734,0.4641616,0.0003169072,0.001122839,0.0000174476,0.0001588917,0.000722288,0.0002965163,0.4145889,0.01060924,0.106878],"study_design_scores_gemma":[0.00256083,0.000208685,0.3709338,0.0006390804,0.0006690371,0.001006457,0.01164704,0.01689726,0.0004351352,0.1253694,0.4687431,0.0008900711],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8997402,0.02240161,0.0507165,0.007683158,0.00295374,0.0004613684,0.00004940425,0.0001137083,0.01588028],"genre_scores_gemma":[0.9892,0.008921853,0.00003078203,0.0001355164,0.0006097098,0.000003092189,0.00002529633,0.00001529832,0.00105844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4581339,"threshold_uncertainty_score":0.4293236,"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."}}