{"id":"W3161235961","doi":"10.1111/rego.12406","title":"Fine me if you can: Fixed asset intensity and enforcement of environmental regulations in China","year":2021,"lang":"en","type":"article","venue":"Regulation & Governance","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Asset (computer security); Enforcement; China; Business; Instrumental variable; Punitive damages; Fixed asset; Sample (material); Fixed effects model; Industrial organization; Panel data; Economics; Econometrics; Microeconomics; Production (economics); Computer security; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0001077361,0.0001302378,0.0002194261,0.00005048828,0.0001035587,0.00004370313,0.00006676054,0.0000399639,0.0005592097],"category_scores_gemma":[0.00005807241,0.000136054,0.00004686675,0.0002789053,0.0000751113,0.0004020255,0.0001387787,0.00005920039,0.000008759435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131714,"about_ca_system_score_gemma":0.00001460964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002304599,"about_ca_topic_score_gemma":0.0005281375,"domain_scores_codex":[0.9990058,0.00000772944,0.0003231816,0.0002474329,0.0002720451,0.0001437884],"domain_scores_gemma":[0.9993237,0.00001303115,0.0003820306,0.0002123829,0.00005938578,0.000009462817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001049842,0.0002144165,0.7444066,0.0001810194,0.0000648808,0.000009009684,0.0004586117,0.007922105,0.009053802,0.2155318,0.003259499,0.01879322],"study_design_scores_gemma":[0.0005384766,0.000004521977,0.9715861,0.0000697795,0.00001480117,0.000002245974,0.0001153448,0.01198168,0.000547467,0.00338018,0.01162399,0.00013538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9892042,0.0003425754,0.0003778944,0.003271056,0.0001645918,0.0001937689,0.00002813733,0.00002794171,0.00638978],"genre_scores_gemma":[0.9976684,0.00004688771,0.0002071304,0.0001536458,0.0001271811,0.000009104579,0.00009597755,0.00001026002,0.001681446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2271795,"threshold_uncertainty_score":0.6122954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01071043713571397,"score_gpt":0.2022074042809274,"score_spread":0.1914969671452134,"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."}}