{"id":"W2125067049","doi":"10.1007/s10708-009-9313-6","title":"Divergent growth trajectories in China’s chemical industry: the case of the newly developed industrial parks in Shanghai, Nanjing and Ningbo","year":2009,"lang":"en","type":"article","venue":"GeoJournal","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"East China Normal University; National Natural Science Foundation of China; University of Toronto; Deutsche Forschungsgemeinschaft; Strong","keywords":"Industrialisation; China; Government (linguistics); Economic geography; Industrial policy; Economic system; Business; Divergence (linguistics); Face (sociological concept); Economy; Economics; Economic growth; Geography; Market economy; International trade","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005543618,0.0001385062,0.000228545,0.001437714,0.002215876,0.002504198,0.0005842182,0.0005494854,0.002412664],"category_scores_gemma":[0.001129937,0.0001389185,0.0002634274,0.002396543,0.002696112,0.001048405,0.002040481,0.0005555723,0.0001092178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007540719,"about_ca_system_score_gemma":0.004636902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2768797,"about_ca_topic_score_gemma":0.5284311,"domain_scores_codex":[0.999606,0.00008438688,0.0000115334,0.00003698305,0.00003905473,0.0002221058],"domain_scores_gemma":[0.9990519,0.0001627864,0.0001961125,0.00003837512,0.0001516545,0.0003991457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003608783,0.0001776367,0.9102696,0.00004056931,0.0000685066,0.002322346,0.03354809,0.004483938,0.001225513,0.034678,0.001140887,0.01168395],"study_design_scores_gemma":[0.00003268182,0.00006380436,0.9312655,0.00002010449,0.00003044707,0.00009537061,0.0578739,0.004968563,0.0002651744,0.002617758,0.002738141,0.00002862229],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979939,0.00003398516,0.00002498535,0.0001894947,8.11285e-7,0.000003215758,0.00003517495,0.000001223919,0.001717056],"genre_scores_gemma":[0.9996241,0.00002209328,0.00001277772,0.000007463223,4.107486e-7,0.000001195725,0.00002456044,4.412068e-7,0.0003069644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2768797,"threshold_uncertainty_score":0.5505361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249453599962794,"score_gpt":0.2701108221947167,"score_spread":0.2476162861950888,"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."}}