{"id":"W3190623100","doi":"","title":"Markets, Human Capital and Inequality: Evidence from Rural China","year":2002,"lang":"en","type":"article","venue":"RePEc: Research Papers in Economics","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":"College of Pharmacy, University of Michigan; European Commission; University of Michigan","keywords":"Economics; Planned economy; Argument (complex analysis); Distribution (mathematics); Investment (military); Market economy; Factor market; Inequality; Income distribution; China; Economic inequality; Black market; Emerging markets; Macroeconomics","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":[0.001194139,0.0002518592,0.0003089407,0.001999697,0.001129398,0.0007110673,0.0004530184,0.0002757164,0.002855706],"category_scores_gemma":[0.002705402,0.0001665256,0.0002447755,0.003986483,0.001632614,0.0005354211,0.001296583,0.0003294924,0.0001603035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008342572,"about_ca_system_score_gemma":0.001145947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09704795,"about_ca_topic_score_gemma":0.1362787,"domain_scores_codex":[0.9995409,0.000125939,0.00002431072,0.00006506965,0.00007626677,0.0001675059],"domain_scores_gemma":[0.9959402,0.0009145725,0.001746717,0.0002666596,0.0004506797,0.0006811619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008343995,0.00007960559,0.9859706,0.00005733797,0.00008955198,0.0002196179,0.003519735,0.0001633982,0.0001383898,0.0009270363,0.0005737531,0.008177595],"study_design_scores_gemma":[0.000006761876,0.00003284962,0.9975321,0.00001646371,0.00001762415,0.00003063949,0.001459628,0.0001312658,0.00003331259,0.0001618742,0.000574268,0.00000311846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973793,0.0004536967,0.0000457878,0.0003694305,0.000002211194,0.000005785622,0.0002050224,0.000001180214,0.001537529],"genre_scores_gemma":[0.9991814,0.0003825037,0.00001509983,0.00005154938,0.000007349868,0.000004103313,0.0001356932,6.038642e-7,0.0002215616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09704795,"threshold_uncertainty_score":0.1929662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04107342058890532,"score_gpt":0.3345397693986431,"score_spread":0.2934663488097378,"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."}}