{"id":"W192735440","doi":"","title":"Markets, Human Capital and Income Inequality in Rural China","year":2001,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inequality; Economics; Human capital; Economic inequality; Income distribution; Income inequality metrics; Rural area; Distribution (mathematics); China; Demographic economics; Labour economics; Development economics; Geography; Economic growth; Political science","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":[0.0004669725,0.0001634077,0.0001603699,0.001318411,0.001242115,0.001019837,0.0003008411,0.0002202873,0.002097639],"category_scores_gemma":[0.0008140397,0.00007258403,0.0001555374,0.001872877,0.001274908,0.0007132613,0.0009333782,0.00036732,0.00006959925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001828549,"about_ca_system_score_gemma":0.001196021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05923436,"about_ca_topic_score_gemma":0.07248923,"domain_scores_codex":[0.9998129,0.00003109042,0.0000072891,0.00002115408,0.0000298148,0.00009775578],"domain_scores_gemma":[0.9995203,0.00008372475,0.000173061,0.00001607014,0.00004361627,0.0001632582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007067755,0.0001707304,0.954793,0.00005008745,0.00004690473,0.0006147088,0.004838581,0.002263702,0.0005194067,0.0189329,0.000724014,0.01697534],"study_design_scores_gemma":[0.000005214266,0.00003933689,0.987609,0.00001785592,0.000008632379,0.00005817947,0.002675301,0.003072332,0.0000809562,0.005476781,0.0009495409,0.00000684309],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962133,0.0002658892,0.00007793309,0.0005195451,0.00000297448,0.000004869666,0.00006262088,0.000001790733,0.00285102],"genre_scores_gemma":[0.9996006,0.00009688394,0.00001463634,0.0000144618,0.000003020259,0.00000226037,0.00002031028,2.93289e-7,0.0002476353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05923436,"threshold_uncertainty_score":0.1177791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006200331415857283,"score_gpt":0.2703424449180196,"score_spread":0.2641421135021623,"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."}}