{"id":"W3026174851","doi":"10.1111/jfir.12217","title":"U.S. POLITICAL CORRUPTION AND LOAN PRICING","year":2020,"lang":"en","type":"article","venue":"The Journal of Financial Research","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Queen's University; Memorial University of Newfoundland","funders":"Social Sciences and Humanities Research Council of Canada; Memorial University of Newfoundland; Concordia University","keywords":"Language change; Loan; Politics; Political corruption; State (computer science); Monetary economics; Business; Economics; Financial system; Finance; Political science; Law","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":[0.0006108039,0.00008350679,0.0001975237,0.0009223779,0.0004242114,0.001087295,0.0001627066,0.00028758,0.005493246],"category_scores_gemma":[0.008421427,0.0001084686,0.0001348894,0.002004236,0.0003618044,0.000377368,0.0005404518,0.000723101,0.0005570013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008272151,"about_ca_system_score_gemma":0.0006304435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03799384,"about_ca_topic_score_gemma":0.05187635,"domain_scores_codex":[0.9994233,0.0002149394,0.00004056491,0.00005335922,0.0001299554,0.0001379981],"domain_scores_gemma":[0.9854703,0.003389139,0.008256277,0.0004589866,0.00146333,0.0009619337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005950062,0.0001277745,0.9827375,0.00001156383,0.00003932533,0.00005729534,0.0002138737,0.0009250955,0.00007239419,0.002369661,0.00525435,0.008131514],"study_design_scores_gemma":[0.000008081081,0.00002313153,0.9939669,0.00002263531,0.00001999986,0.00006306227,0.0004444839,0.002482103,0.0001387113,0.001105023,0.001719126,0.000006619658],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881437,0.0002740039,0.0002382731,0.001500455,0.00001302504,0.000008643645,0.001286383,0.00001706723,0.00851846],"genre_scores_gemma":[0.9991328,0.0000697213,0.00002909694,0.00005205367,0.000008336987,0.000002528549,0.0002889143,0.000002197424,0.0004143717],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03799384,"threshold_uncertainty_score":0.07554537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1728875413433134,"score_gpt":0.4184614436787802,"score_spread":0.2455739023354669,"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."}}