{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002247104,0.0001519843,0.0004065467,0.0004679657,0.0006501058,0.0007475594,0.0003048062,0.0004986695,0.00477179],"category_scores_gemma":[0.01354663,0.0001033137,0.0001936281,0.000629153,0.0009100196,0.0005266063,0.0007336616,0.0004571688,0.0003590237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008074867,"about_ca_system_score_gemma":0.002769413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007934413,"about_ca_topic_score_gemma":0.02522083,"domain_scores_codex":[0.9975888,0.00140788,0.0000595098,0.0001096425,0.0003474661,0.0004867939],"domain_scores_gemma":[0.9870841,0.003574347,0.006297126,0.0009863353,0.000920116,0.001138056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005774598,0.003678132,0.7578557,0.0003917079,0.0002095673,0.0002301928,0.002729032,0.00310112,0.005983074,0.0152498,0.003131076,0.2068633],"study_design_scores_gemma":[0.0001241295,0.001271507,0.9792547,0.0001185457,0.00008566735,0.0001128667,0.002847611,0.002929608,0.002418318,0.005005102,0.005819038,0.00001285064],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807646,0.0003281049,0.0005208877,0.003817475,0.00001000604,0.00004730326,0.00009022652,0.00001492211,0.01440639],"genre_scores_gemma":[0.9988422,0.000150971,0.0001707261,0.0001855679,0.000009362268,0.00001022493,0.00001593014,0.000001408497,0.0006136326],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007934413,"threshold_uncertainty_score":0.01596326,"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."}}