{"id":"W1566372836","doi":"","title":"China's Income Distribution and Inequality","year":2004,"lang":"en","type":"article","venue":"Econometric Society 2004 North American Summer Meetings","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Inequality; Income inequality metrics; Economic inequality; Economics; Income distribution; Gini coefficient; China; Household income; Distribution (mathematics); Welfare; Demographic economics; Survey data collection; Rural area; Proxy (statistics); Geography; Econometrics; Statistics; Political science; Mathematics","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":[0.001924892,0.0002867587,0.0005389409,0.0001252635,0.001051153,0.000160863,0.0003559351,0.0001108101,0.0001005272],"category_scores_gemma":[0.0009434759,0.000299786,0.0002945865,0.003445872,0.001378351,0.0005150504,0.0001316601,0.0003416818,0.00006075285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009362675,"about_ca_system_score_gemma":0.0003064899,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07520242,"about_ca_topic_score_gemma":0.003813814,"domain_scores_codex":[0.9973011,0.0002110012,0.0006027239,0.0006057242,0.0004536238,0.0008258536],"domain_scores_gemma":[0.9983549,0.0002101759,0.0005353694,0.0003243241,0.0001433457,0.0004318648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000125118,0.0001317585,0.9751803,0.00003152246,0.00006439171,0.000001420438,0.009276727,0.00006469509,0.000001311405,0.00244044,0.001367914,0.01142707],"study_design_scores_gemma":[0.0006110923,0.000101613,0.9674271,0.0000110276,0.00002703574,0.000001061775,0.004639668,0.00001955731,0.00001713275,0.0005908961,0.02610431,0.000449503],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989633,0.0004156886,0.001052223,0.002507257,0.000276883,0.0003026896,0.0001711537,0.0001761042,0.005464986],"genre_scores_gemma":[0.9963503,0.0008862772,0.0007604893,0.001250682,0.0004301726,0.00002595522,0.0001073864,0.00002398498,0.0001646978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0713886,"threshold_uncertainty_score":0.9999454,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01996554482147558,"score_gpt":0.2831117346678396,"score_spread":0.2631461898463641,"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."}}