{"id":"W1488247520","doi":"10.1111/deci.12093","title":"Producing Synergy: Innovation, IT, and Productivity","year":2014,"lang":"en","type":"article","venue":"Decision Sciences","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Calgary; Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Productivity; Industrial organization; Stock (firearms); Investment (military); Business; Information technology; Economics; Computer science; Economic growth; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001691418,0.0003600584,0.0003999629,0.002540036,0.0005241225,0.002814854,0.0003205078,0.000682139,0.007360842],"category_scores_gemma":[0.009532434,0.0001549966,0.0004962154,0.002778222,0.00133789,0.001541402,0.001939247,0.0007955616,0.0005275158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244356,"about_ca_system_score_gemma":0.001135127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003013872,"about_ca_topic_score_gemma":0.002847206,"domain_scores_codex":[0.9988564,0.0004529549,0.0000650587,0.0001480457,0.0002327574,0.0002448299],"domain_scores_gemma":[0.9779604,0.01294306,0.005643853,0.0005782252,0.0008640478,0.002010315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002423317,0.0004383773,0.9138465,0.0001520339,0.0005565728,0.0005120548,0.0005180196,0.02171747,0.001227198,0.01753697,0.001378741,0.04187378],"study_design_scores_gemma":[0.00008570855,0.0004458074,0.9118654,0.0002083176,0.0005396924,0.0002844747,0.002371135,0.01973371,0.001835278,0.05669889,0.005888952,0.0000425869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804313,0.0007100512,0.002226819,0.00114616,0.00001194231,0.00001787029,0.00028344,0.00002314469,0.01514928],"genre_scores_gemma":[0.9991243,0.0001685835,0.000193274,0.00002778445,0.00001584264,0.000005326962,0.00005399393,0.000001124264,0.0004096813],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007360842,"threshold_uncertainty_score":0.02462447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06242734571149087,"score_gpt":0.2659844121686588,"score_spread":0.2035570664571679,"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."}}