{"id":"W2034875705","doi":"10.1017/s0305741014000010","title":"State-Led Urbanization in China: Skyscrapers, Land Revenue and “Concentrated Villages”","year":2014,"lang":"en","type":"article","venue":"The China Quarterly","topic":"Urbanization and City Planning","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Urbanization; China; Revenue; State (computer science); Business; Natural resource economics; Geography; Economic geography; Agricultural economics; Economy; Economics; Economic growth; Finance; Archaeology","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":[0.0004427778,0.0000670012,0.00009783875,0.0007760592,0.0009345164,0.0009662344,0.000230616,0.0001812675,0.003153399],"category_scores_gemma":[0.0005830784,0.00006281039,0.0001372555,0.0009974317,0.00195154,0.0003638361,0.0008888415,0.000238825,0.00004874135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002941252,"about_ca_system_score_gemma":0.003466353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04784152,"about_ca_topic_score_gemma":0.141394,"domain_scores_codex":[0.9997304,0.00007336299,0.000009175104,0.00002651825,0.00004797298,0.0001124824],"domain_scores_gemma":[0.9995385,0.00007482491,0.0001856098,0.0000260291,0.00006000963,0.0001149814],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000131493,0.0002421602,0.7895659,0.0001070755,0.00006574546,0.0020514,0.01292685,0.002761763,0.0008964935,0.1501089,0.003198179,0.03794416],"study_design_scores_gemma":[0.00002204327,0.0001143103,0.9673824,0.00004363965,0.00003328054,0.0001256478,0.01271095,0.002815225,0.0002924489,0.007256838,0.009184457,0.00001875495],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991791,0.0001121559,0.00005911442,0.0006962569,0.00000424182,0.000006945981,0.00002194364,0.000002624797,0.007305552],"genre_scores_gemma":[0.9994397,0.00004118998,0.000008957033,0.00001528697,0.000002487319,0.000001274791,0.000006688781,2.585807e-7,0.0004842193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04784152,"threshold_uncertainty_score":0.09512609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005123789706976467,"score_gpt":0.2282738405195956,"score_spread":0.2231500508126191,"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."}}