{"id":"W4385212501","doi":"10.5465/amproc.2023.10645symposium","title":"Insights on the Determinants of Scientific Knowledge Production","year":2023,"lang":"en","type":"article","venue":"Academy of Management Proceedings","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Production (economics); Knowledge production; Data science; Knowledge management; Computer science; Business; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02657146,0.000560571,0.0008646367,0.008068332,0.002913764,0.0187392,0.001682027,0.001866612,0.01119785],"category_scores_gemma":[0.1105399,0.0007908783,0.0009972873,0.009404292,0.01095737,0.01523966,0.008148746,0.002879194,0.001382159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006646884,"about_ca_system_score_gemma":0.008591641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004667326,"about_ca_topic_score_gemma":0.003395044,"domain_scores_codex":[0.9785551,0.01084386,0.001291201,0.002041148,0.004754115,0.00251454],"domain_scores_gemma":[0.7669949,0.1929393,0.01615705,0.01013255,0.008755919,0.005020289],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002217464,0.0004397912,0.2996714,0.0005654349,0.0001968981,0.0008792646,0.02595633,0.00492496,0.0008732295,0.5756611,0.003394957,0.08721472],"study_design_scores_gemma":[0.00007663867,0.0001175584,0.2043114,0.0005252326,0.0001150386,0.0003343927,0.03139049,0.007177717,0.001467881,0.7210024,0.03340296,0.00007818871],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6101119,0.008492265,0.03740972,0.07770045,0.0002205761,0.0002789784,0.001214609,0.0001610699,0.2644105],"genre_scores_gemma":[0.9939406,0.001677847,0.002073752,0.0003680234,0.00009031686,0.00007269799,0.0001522485,0.00002486359,0.001599656],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9734285,"threshold_uncertainty_score":0.140525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0698446543620156,"score_gpt":0.256655118988918,"score_spread":0.1868104646269024,"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."}}