{"id":"W2195650834","doi":"10.1007/978-981-10-6286-5_8","title":"Hub-Periphery Development Pattern and Inclusive Growth: Case Study of Guangdong Province","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"China; Comparative advantage; Inclusive growth; Sustainable development; Economic geography; Production (economics); Business; Economy; Geography; Economic system; Economic growth; International trade; Political science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002985255,0.0003642303,0.00107055,0.0003284086,0.0002831125,0.0001105832,0.0003240383,0.0002183032,0.0004696386],"category_scores_gemma":[0.00001942415,0.0003876218,0.0001579874,0.00001115111,0.0001078738,0.0001551606,0.0004833178,0.000191707,0.00008980047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001425753,"about_ca_system_score_gemma":0.00008529675,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01088773,"about_ca_topic_score_gemma":0.01857833,"domain_scores_codex":[0.9979169,0.000004986335,0.001112036,0.0007204296,0.00005304585,0.0001925486],"domain_scores_gemma":[0.9978191,0.00004027036,0.001386987,0.0005568957,0.00008718505,0.0001096112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005802219,0.0007736679,0.1084234,0.0007007476,0.003742828,0.004586595,0.007375775,0.00004809201,0.000001475835,0.7926863,0.00114518,0.08045788],"study_design_scores_gemma":[0.01282994,0.003639948,0.02493835,0.001029126,0.001128937,0.003343679,0.008416052,0.007185649,0.00008586417,0.3535819,0.5705486,0.01327192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3104109,0.00132968,0.0006009901,0.0003376183,0.0002487977,0.0008711192,0.0001632983,0.00002303246,0.6860145],"genre_scores_gemma":[0.8508921,0.0003567898,0.0001542289,0.00009248096,0.00009491135,0.00001974798,0.00002132177,0.00005093714,0.1483174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5694034,"threshold_uncertainty_score":0.9998575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03886217208869303,"score_gpt":0.2235995791314133,"score_spread":0.1847374070427203,"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."}}