{"id":"W1512796984","doi":"10.3386/w19538","title":"Credit History: The Changing Nature of Scientific Credit","year":2013,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Business; Credit history; Financial system; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.00969345,0.0002747922,0.00057784,0.004512105,0.003306689,0.01093873,0.001694808,0.003245666,0.01307723],"category_scores_gemma":[0.05886408,0.0004472877,0.0003961586,0.008246734,0.01057388,0.01616635,0.003286442,0.003894299,0.001210232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008621342,"about_ca_system_score_gemma":0.004692362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00629864,"about_ca_topic_score_gemma":0.004523804,"domain_scores_codex":[0.994041,0.00213206,0.0003164299,0.0009230233,0.001958848,0.0006285252],"domain_scores_gemma":[0.9544989,0.02147503,0.007929409,0.004528409,0.006176917,0.005391273],"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":[0.0001115294,0.00006074605,0.01500378,0.000107569,0.00002602355,0.0001480118,0.00181254,0.005419379,0.0005166783,0.9046029,0.004930984,0.06725988],"study_design_scores_gemma":[0.00004963552,0.00004198858,0.01457316,0.000172101,0.00001587173,0.0001096153,0.0008534112,0.00428923,0.0004028604,0.9153484,0.06409032,0.00005335885],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2721376,0.01787562,0.1532862,0.1062908,0.001675913,0.0001582381,0.0009579431,0.0002771262,0.4473405],"genre_scores_gemma":[0.9735081,0.003959541,0.005186781,0.001427181,0.0009765312,0.00005157128,0.0001443029,0.00007336903,0.0146727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9954879,"threshold_uncertainty_score":0.06255245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8350192033811803,"score_gpt":0.655006246116735,"score_spread":0.1800129572644453,"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."}}