{"id":"W2891403035","doi":"10.3386/w25098","title":"Corruption, Government Subsidies, and Innovation: Evidence from China","year":2018,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Innovation Policy and R&D","field":"Economics, Econometrics and Finance","cited_by":127,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Subsidy; Language change; Government (linguistics); China; Politics; Quarter (Canadian coin); Business; Public economics; Economics; Private sector; Rent-seeking; Market economy; Economic growth; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001657757,0.0003047643,0.0004650364,0.002108335,0.0009263314,0.0009629669,0.0004139239,0.0004529291,0.002438269],"category_scores_gemma":[0.003506245,0.0001858316,0.0006274428,0.003729303,0.001070754,0.0004680344,0.000964232,0.0004693177,0.0002364235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00185509,"about_ca_system_score_gemma":0.00385686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2023823,"about_ca_topic_score_gemma":0.2392162,"domain_scores_codex":[0.9991668,0.0001733699,0.00007605841,0.0001022668,0.00020604,0.0002753917],"domain_scores_gemma":[0.9915638,0.001709378,0.004155836,0.0004263802,0.001158892,0.0009857118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006428302,0.00009565509,0.9911975,0.00006184614,0.0001271005,0.0001812758,0.0004183137,0.0003201422,0.00009119014,0.0004966723,0.0008505281,0.006095541],"study_design_scores_gemma":[0.0000171984,0.00003053873,0.9980727,0.00002464414,0.00009947798,0.00003155348,0.0003955655,0.0004291083,0.00007016259,0.00009338758,0.0007295943,0.000005970421],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965203,0.0007923141,0.00005658674,0.0005325908,0.000005936071,0.00001221982,0.0004552388,0.000004034482,0.001620838],"genre_scores_gemma":[0.9982973,0.0006576751,0.00003465527,0.00008766025,0.00001419621,0.000006043471,0.0004553411,0.000001489083,0.0004458423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2023823,"threshold_uncertainty_score":0.4024086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5495381632431763,"score_gpt":0.5011008292003394,"score_spread":0.0484373340428369,"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."}}