{"id":"W2735618726","doi":"10.2139/ssrn.2996009","title":"The Economic Impact of China's Anti-Corruption Campaign","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"China; Language change; Political science; Development economics; Economics; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.003748097,0.00007891304,0.0001276601,0.00004115355,0.002523109,0.0002921844,0.0005734089,0.00004829794,0.0001916542],"category_scores_gemma":[0.000108127,0.0000562469,0.0001714527,0.00001780518,0.0001871182,0.0003112744,0.00003800705,0.0004585571,0.00009710579],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004466597,"about_ca_system_score_gemma":0.007836603,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004492121,"about_ca_topic_score_gemma":0.01844192,"domain_scores_codex":[0.9983361,0.00007700754,0.0002417086,0.00009918803,0.0001073461,0.001138687],"domain_scores_gemma":[0.999202,0.0000327178,0.0004398247,0.0002130028,0.00003238357,0.00008005293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00008510405,0.00002999031,0.1195416,0.000001623652,0.0002993733,0.000001010828,0.004622767,0.0004768225,0.0001089011,0.3730994,0.0003567115,0.5013767],"study_design_scores_gemma":[0.001034454,0.000255591,0.8325121,0.00002212692,0.00003070536,0.00007783256,0.01050664,0.0004175572,0.00001840118,0.1225559,0.03222973,0.0003389178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980474,0.0002239211,0.0002406582,0.00111586,0.0007494804,0.00008807915,0.000001412919,0.0000103041,0.01709632],"genre_scores_gemma":[0.9858085,0.009464948,0.000007677434,0.000007120813,0.0003426905,0.000001820034,5.147425e-7,0.000006746466,0.004360016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7129706,"threshold_uncertainty_score":0.999469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388018591383222,"score_gpt":0.3164911506986661,"score_spread":0.3026109647848339,"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."}}