{"id":"W1999894924","doi":"10.1080/1351847042000254211","title":"Generating science-based growth: an econometric analysis of the impact of organizational incentives on university–industry technology transfer","year":2005,"lang":"en","type":"article","venue":"European Journal of Finance","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Trent University; Nottingham Trent University; Alfred P. Sloan Foundation","keywords":"Incentive; Technology transfer; Intellectual property; Knowledge transfer; Payment; Business; Function (biology); Marketing; Industrial organization; Production (economics); Economics; Knowledge management; Microeconomics; Management; Finance; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031525,0.00007704264,0.000257497,0.002440524,0.0001243112,0.00001397173,0.0005056057,0.00004041273,0.0001259153],"category_scores_gemma":[0.0002161516,0.00006513704,0.0001524351,0.006845816,0.0002593849,0.0002747867,0.00002296264,0.0002272719,0.000004946425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263484,"about_ca_system_score_gemma":0.0001496535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008101952,"about_ca_topic_score_gemma":0.000001416739,"domain_scores_codex":[0.9990617,0.00004923913,0.0005737383,0.0001337897,0.00005741949,0.0001241346],"domain_scores_gemma":[0.9987781,0.00002670754,0.0007046955,0.0001754852,0.0002882197,0.00002681329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005274563,0.0003962046,0.3131858,0.000007656329,0.0003163902,0.000005477155,0.0005047817,0.3873324,0.002264998,0.2931449,0.00008771972,0.002700899],"study_design_scores_gemma":[0.0008584464,0.0004014088,0.9682451,0.00003450479,0.00004587961,0.00000495563,0.00007233884,0.01946728,0.009700473,0.0003228954,0.0006859665,0.0001607538],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928449,0.00006999946,0.003249341,0.0004739836,0.00005711326,0.00002879124,0.00007771599,0.000002719481,0.003195439],"genre_scores_gemma":[0.9989223,0.00002314365,0.000836342,0.00007147805,0.00005697096,3.205021e-8,0.000001372748,0.000007570427,0.00008077216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6550593,"threshold_uncertainty_score":0.3289188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02327371765046747,"score_gpt":0.2243113527724045,"score_spread":0.2010376351219371,"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."}}