{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005461167,0.0001042469,0.000241741,0.0003801753,0.0003360465,0.0001758045,0.0002380684,0.00004173152,0.00008179886],"category_scores_gemma":[0.003412988,0.0001003287,0.00002273779,0.0009345775,0.0002466441,0.0007000928,0.00009788248,0.00008381014,0.0001823587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001901274,"about_ca_system_score_gemma":0.00002320272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007749264,"about_ca_topic_score_gemma":0.00002704117,"domain_scores_codex":[0.9984195,0.00001705287,0.0004616733,0.0008242895,0.0000550195,0.0002225086],"domain_scores_gemma":[0.999106,0.0001908272,0.000257248,0.0003415896,0.0000558384,0.0000484774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001023583,0.00004710058,0.245039,0.00001279531,0.000006228616,2.421815e-7,0.0005165142,0.0000957413,0.0001505872,0.6704435,0.003905194,0.07977281],"study_design_scores_gemma":[0.0002093803,0.00009454926,0.2341568,0.00001454126,0.000001315071,0.000009552818,0.00004068664,0.002438604,0.0007251467,0.6369291,0.1251103,0.0002700622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9554303,0.0003217238,0.01773173,0.004097973,0.0006832075,0.0001139105,0.000006583685,0.00002886833,0.02158576],"genre_scores_gemma":[0.9932506,0.00002370384,0.005717323,0.0003852153,0.0002207193,0.000008190775,0.000001093943,0.000006071258,0.0003870971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1212051,"threshold_uncertainty_score":0.4091285,"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."}}