{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03240479,0.0001622433,0.0003682046,0.0007835666,0.0003824543,0.0001842211,0.003609964,0.0002851575,0.0002273647],"category_scores_gemma":[0.01159315,0.00009732055,0.0001483963,0.0006188481,0.001096831,0.0002573829,0.0006824574,0.0005706834,0.000165438],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008075872,"about_ca_system_score_gemma":0.007298438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001250461,"about_ca_topic_score_gemma":0.0001833704,"domain_scores_codex":[0.9936848,0.0003191762,0.001240448,0.000721961,0.003745377,0.0002882787],"domain_scores_gemma":[0.9871306,0.004994957,0.0008680906,0.002108183,0.004767274,0.0001309255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006563814,0.00007565903,0.01073785,0.00001328582,0.00005117549,1.383012e-7,0.00004474368,0.0002959635,0.0000315111,0.006351677,0.949591,0.03274132],"study_design_scores_gemma":[0.0002078886,0.001394735,0.0555476,0.0001415523,0.000009611885,0.00001682243,0.00004665151,0.05858905,0.0004948467,0.7866168,0.09668896,0.0002454644],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4711921,0.0001740898,0.00004020826,0.001870876,0.0006151068,0.001241955,0.001514128,0.00003991056,0.5233117],"genre_scores_gemma":[0.9934209,0.0003965629,0.000452206,0.000008657636,0.001316468,0.00005974614,0.0009448632,0.00002348147,0.003377117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8529021,"threshold_uncertainty_score":0.9983293,"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."}}