{"id":"W2591858705","doi":"10.1371/journal.pone.0173607","title":"Ownership reform and the changing manufacturing landscape in Chinese cities: The case of Wuxi","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Priority Academic Program Development of Jiangsu Higher Education Institutions; Nanjing University; Nanjing University of Posts and Telecommunications; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Marketization; Suburbanization; Economic geography; Decentralization; China; Distribution (mathematics); Business; Government (linguistics); Local government; Manufacturing; Economic system; Industrial organization; Geography; Market economy; Economics; Marketing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004926508,0.0001629975,0.0002416389,0.001226595,0.003795822,0.001783316,0.0008209254,0.0005475729,0.001387491],"category_scores_gemma":[0.0007065345,0.000190565,0.0002949599,0.003101757,0.004025777,0.0008957137,0.002245184,0.0005015669,0.00007097066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0107005,"about_ca_system_score_gemma":0.003487195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2412681,"about_ca_topic_score_gemma":0.3750206,"domain_scores_codex":[0.9994387,0.0001621771,0.00001642369,0.0000528797,0.00006412738,0.0002656293],"domain_scores_gemma":[0.999635,0.00006318461,0.0001012424,0.00005821855,0.00004702284,0.00009528489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002874958,0.0003406614,0.6227326,0.0002096673,0.0001339175,0.02387284,0.1730884,0.01167713,0.004270726,0.1199086,0.002472871,0.04100522],"study_design_scores_gemma":[0.00004122644,0.0001481683,0.7482002,0.00008560331,0.0001004288,0.001120557,0.2071286,0.01534319,0.001289633,0.007411516,0.01904992,0.00008097844],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975439,0.0000626849,0.00008923591,0.0002263978,0.000001597246,0.000007375666,0.00001533241,0.000001758969,0.002051804],"genre_scores_gemma":[0.9994488,0.00004527717,0.00004609279,0.00001521728,9.45704e-7,0.000003304469,0.00001217463,6.98885e-7,0.0004274846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2412681,"threshold_uncertainty_score":0.4797275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063933786821966,"score_gpt":0.2630666411530508,"score_spread":0.2324273032848311,"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."}}