{"id":"W3165047795","doi":"10.1016/j.jclepro.2021.127592","title":"Social insurance contributions ratio and productivity of private enterprises in the heavy pollution industry: Evidence from China","year":2021,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Productivity; Business; Social insurance; Panel data; Investment (military); Crowding out; Crowds; China; Labour economics; Industrial organization; Natural resource economics; Economics; Economic growth; Market economy; Monetary economics","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":[0.001361273,0.00009866664,0.0003306363,0.0001211499,0.00009430706,0.0000393976,0.000130497,0.0001047043,0.00002280462],"category_scores_gemma":[0.001138613,0.00009447003,0.00007005024,0.0001952837,0.0001162196,0.0007483843,0.00003743092,0.0004183958,0.000002544675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001536607,"about_ca_system_score_gemma":0.00003820043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007249,"about_ca_topic_score_gemma":0.00003719524,"domain_scores_codex":[0.9986654,0.0001323574,0.000740917,0.0002652083,0.00006683129,0.0001292723],"domain_scores_gemma":[0.9983862,0.00004031847,0.001289042,0.0002081326,0.00004926886,0.00002704381],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001773491,0.0004245369,0.9730306,0.0000366926,0.00009488193,0.000006768597,0.002651965,0.004312564,0.009406385,0.007030488,0.0006466167,0.002181168],"study_design_scores_gemma":[0.0003586469,0.00005398448,0.9738199,0.00005685271,0.00001147977,0.00004138015,0.0001775338,0.00005589682,0.0143888,0.009453947,0.001491596,0.00009000284],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9807392,0.001972741,0.0007592445,0.01587677,0.0003985779,0.0001175687,0.00005254737,0.000003182403,0.00008014376],"genre_scores_gemma":[0.9983696,0.0007301121,0.0001416597,0.0000468734,0.0006336935,0.000004628712,0.000004262162,0.000008325508,0.0000608941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01763033,"threshold_uncertainty_score":0.3852376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02720074639141853,"score_gpt":0.242769483067201,"score_spread":0.2155687366757825,"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."}}