{"id":"W3122008452","doi":"","title":"Incentives for Accuracy in Analyst Research","year":2011,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Incentive; Business; Computer science; Economics; Microeconomics","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"],"consensus_categories":[],"category_scores_codex":[0.02604836,0.001011555,0.001171373,0.002276623,0.001838439,0.007004037,0.002313477,0.005968184,0.01300053],"category_scores_gemma":[0.1195278,0.00134337,0.0008825012,0.002226995,0.003352489,0.008491713,0.002638741,0.002980109,0.001765802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006581561,"about_ca_system_score_gemma":0.005570792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003501696,"about_ca_topic_score_gemma":0.002026319,"domain_scores_codex":[0.9767712,0.01311322,0.001202006,0.002497119,0.004109125,0.0023072],"domain_scores_gemma":[0.8503013,0.08967564,0.03948276,0.01034575,0.005481425,0.004713046],"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":[0.0008167498,0.000537122,0.02711471,0.0002839267,0.0001158221,0.0004492709,0.001847071,0.08431526,0.002240288,0.8289843,0.003185203,0.05011026],"study_design_scores_gemma":[0.00086452,0.0007366067,0.02536955,0.0002155966,0.0001309751,0.0008689638,0.0008478917,0.2356813,0.001441775,0.7051162,0.02849565,0.0002310172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4004724,0.002063514,0.4147326,0.02469732,0.0002858098,0.001564724,0.001084942,0.0008105133,0.1542882],"genre_scores_gemma":[0.9643869,0.0003945717,0.02117423,0.0004201215,0.0001542097,0.0003520002,0.00008760632,0.00003444259,0.01299581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9739516,"threshold_uncertainty_score":0.1377586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1164469855337029,"score_gpt":0.389936793807429,"score_spread":0.273489808273726,"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."}}