{"id":"W3121931689","doi":"10.3386/w24334","title":"The Impact of Big Data on Firm Performance: An Empirical Investigation","year":2018,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Econometrics; Product (mathematics); Dimension (graph theory); Scale (ratio); Set (abstract data type); Economics; Data set; Contrast (vision); Computer science; Statistics; Mathematics; Geography","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":[0.009385929,0.0005565786,0.0006759276,0.002806604,0.001018426,0.003838738,0.001646922,0.001979378,0.003205942],"category_scores_gemma":[0.05901558,0.000370332,0.0007330187,0.006382146,0.001926262,0.005031304,0.002207909,0.004873599,0.0009960263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001882807,"about_ca_system_score_gemma":0.001343183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01609416,"about_ca_topic_score_gemma":0.01416901,"domain_scores_codex":[0.9941983,0.00185742,0.0004396026,0.0006723111,0.001794074,0.001038194],"domain_scores_gemma":[0.7851683,0.1499284,0.0410556,0.007620097,0.007377194,0.008850395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003827384,0.000821142,0.9659989,0.0001318031,0.0002469509,0.0004215954,0.0003543149,0.01241923,0.0001676016,0.001875421,0.004526243,0.01265406],"study_design_scores_gemma":[0.00007548281,0.0006754434,0.9494721,0.00014679,0.0001458395,0.0002811543,0.00289068,0.03830164,0.0008306178,0.003119658,0.003993574,0.00006709509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98635,0.001566355,0.0009816957,0.004036475,0.00005305685,0.00004656179,0.002699273,0.00006103721,0.004205501],"genre_scores_gemma":[0.9969546,0.0003573184,0.0002303014,0.0001870877,0.00009866625,0.00001682219,0.001784207,0.00001171625,0.0003592791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01609416,"threshold_uncertainty_score":0.04963815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8833779029714723,"score_gpt":0.6808319464571724,"score_spread":0.2025459565143,"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."}}