{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007749725,0.0002315137,0.0002904786,0.002833965,0.000525422,0.0007039352,0.0003622287,0.000146747,0.002662879],"category_scores_gemma":[0.002712394,0.0001185693,0.0003773214,0.004212386,0.0003609183,0.000567164,0.0007818025,0.0003463101,0.0002355808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001359728,"about_ca_system_score_gemma":0.0009915172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05351616,"about_ca_topic_score_gemma":0.04901837,"domain_scores_codex":[0.9995713,0.00006982777,0.00002994207,0.00008060478,0.0001692252,0.00007920126],"domain_scores_gemma":[0.9991456,0.0001712107,0.0003059978,0.0001295623,0.000180746,0.00006676061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004693566,0.00002902319,0.9121367,0.00005008115,0.0001293733,0.0002291574,0.0006841729,0.007105065,0.0002903163,0.01757342,0.005658154,0.0560676],"study_design_scores_gemma":[0.00001188043,0.00002238568,0.9707015,0.00001467474,0.00002816509,0.00006890862,0.0001620673,0.01792041,0.0002532558,0.003789529,0.007011731,0.00001541904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9572278,0.0005261088,0.01109966,0.0007412887,0.00003818148,0.0000855672,0.008945727,0.0001473215,0.02118825],"genre_scores_gemma":[0.993338,0.000162595,0.001628116,0.00003162062,0.00002511405,0.00005263135,0.003260182,0.000007166554,0.00149466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05351616,"threshold_uncertainty_score":0.1064093,"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."}}