{"id":"W2112136134","doi":"10.1068/a37295","title":"Segmented Local Labor Markets in Postreform China: Gender Earnings Inequality in the Case of Two Towns in Zhejiang Province","year":2006,"lang":"en","type":"article","venue":"Environment and Planning A Economy and Space","topic":"China's Socioeconomic Reforms and Governance","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Lethbridge","funders":"","keywords":"Labor market segmentation; Earnings; China; Market segmentation; Point (geometry); Inequality; Segmentation; Space (punctuation); Labour economics; Economics; Geography; Microeconomics","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":[0.0006793128,0.0002427003,0.0003821389,0.001708321,0.005247559,0.001948513,0.001258638,0.0007862689,0.003024167],"category_scores_gemma":[0.001259307,0.0001938744,0.0003473744,0.00208777,0.003211823,0.001018484,0.002057403,0.0006007866,0.0001213724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007433778,"about_ca_system_score_gemma":0.003241639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.284821,"about_ca_topic_score_gemma":0.4384094,"domain_scores_codex":[0.9992223,0.0001765921,0.00001468028,0.00005157819,0.00006001977,0.0004748992],"domain_scores_gemma":[0.9993404,0.0001462664,0.0001554768,0.00004384528,0.00007601463,0.0002380988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003910372,0.0006358405,0.8002591,0.00008101394,0.00007389506,0.02581318,0.1289302,0.002766918,0.001403509,0.02494078,0.001139853,0.01356459],"study_design_scores_gemma":[0.00004701709,0.0001174898,0.7231147,0.00005113147,0.00004311121,0.0006393772,0.2647842,0.006151749,0.0003344002,0.002341262,0.002343655,0.00003179412],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988971,0.00002297487,0.00002885706,0.0001010809,9.148515e-7,0.000005077836,0.000008415755,7.451329e-7,0.0009348031],"genre_scores_gemma":[0.9996854,0.00001721109,0.0000149043,0.000009830434,9.640016e-7,0.00000320989,0.000009866896,3.65637e-7,0.0002581975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.284821,"threshold_uncertainty_score":0.5663264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008573173155015431,"score_gpt":0.239677638428579,"score_spread":0.2311044652735636,"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."}}