{"id":"W3005889938","doi":"10.1016/j.chieco.2022.101825","title":"Monetary stimulus policy in China: The bank credit channel","year":2022,"lang":"en","type":"article","venue":"China Economic Review","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Economics; Monetary economics; Collateral; Monetary policy; Credit channel; Stimulus (psychology); Loan; China; Macroeconomics; Finance; Inflation targeting","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002722989,0.0002637003,0.0007872665,0.0002868152,0.0003776114,0.00005513457,0.000927289,0.00005060125,0.002907125],"category_scores_gemma":[0.0001940012,0.0002625778,0.000265842,0.0005352509,0.0001102239,0.0002435498,0.000371927,0.0004129564,0.00055194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000931176,"about_ca_system_score_gemma":0.0001109113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001874778,"about_ca_topic_score_gemma":0.00008763034,"domain_scores_codex":[0.9973767,0.0001303046,0.001289807,0.0006927022,0.00005090922,0.0004596075],"domain_scores_gemma":[0.9982002,0.00007916104,0.0006087297,0.001037088,0.000006380493,0.0000684309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000465992,0.000844611,0.1371327,0.002748729,0.0001884508,0.0000170358,0.004679038,0.1251785,0.000001603437,0.6423613,0.03925616,0.04754515],"study_design_scores_gemma":[0.0005809979,0.00008785442,0.6542483,0.0001423745,0.00001543373,0.00003392608,0.00002570912,0.02715372,9.409948e-7,0.09655342,0.2206181,0.0005391568],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.4536761,0.4756334,0.0002271711,0.03009056,0.00212807,0.002391357,0.0005516175,0.0001154207,0.03518628],"genre_scores_gemma":[0.968961,0.02847704,0.00004376525,0.001205627,0.0003479478,0.0002880283,0.00005979191,0.00004582531,0.0005709462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.545808,"threshold_uncertainty_score":0.9999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175478132617636,"score_gpt":0.2452732103394704,"score_spread":0.223518429013294,"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."}}