{"id":"W4377234327","doi":"10.3386/w31266","title":"Does Combating Corruption Reduce Clientelism?","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Agencia Española de Cooperación Internacional para el Desarrollo; University of Toronto; Social Sciences and Humanities Research Council of Canada; Canada Research Chairs; Canadian Institute for Advanced Research","keywords":"Clientelism; Language change; Computer security; Business; Political science; Computer science; Law; Politics; Art; Democracy","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.01797764,0.0001765775,0.0004338195,0.00101617,0.00062398,0.000166005,0.0006273386,0.0004398225,0.002968123],"category_scores_gemma":[0.004137571,0.0001568577,0.0001862519,0.0003124319,0.0004972887,0.0002126254,0.0002480748,0.0006557996,0.002054921],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.006167543,"about_ca_system_score_gemma":0.01147349,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01067607,"about_ca_topic_score_gemma":0.006533602,"domain_scores_codex":[0.9954247,0.000347254,0.0009624792,0.0005896098,0.002098286,0.0005776608],"domain_scores_gemma":[0.9955468,0.001415374,0.0004907469,0.0002369251,0.00212628,0.0001838993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008704363,0.0001755382,0.005957764,0.0002961775,0.000307678,0.000007306895,0.005277311,0.0003929026,0.00006043028,0.4862442,0.3903781,0.1108155],"study_design_scores_gemma":[0.000354832,0.00003682793,0.003636739,0.0002667826,0.00001037234,0.000002297031,0.003952198,0.0002286755,0.00002432794,0.07769372,0.9134206,0.0003725947],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.006627365,0.00006588421,0.00000278724,0.003052684,0.006565106,0.0006474333,0.00009162204,0.00008271178,0.9828644],"genre_scores_gemma":[0.5136372,0.00856181,0.0002189461,0.00004997562,0.004803259,0.0002518265,0.0009398674,0.00008564876,0.4714514],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5230425,"threshold_uncertainty_score":0.9987221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6470580732297617,"score_gpt":0.6095088227074255,"score_spread":0.03754925052233626,"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."}}