{"id":"W3207766122","doi":"10.1002/psp.2524","title":"Migration policies on migrant–native marriage: A multilevel analysis of 43 Chinese cities","year":2021,"lang":"en","type":"article","venue":"Population Space and Place","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"China; Merge (version control); Demographic economics; Internal migration; Census; Migrant workers; Inequality; Logistic regression; Geography; Index of dissimilarity; Population; Political science; Demography; Development economics; Economic geography; Economic growth; Sociology; Economics","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":[0.001580269,0.0004068922,0.000733412,0.001986033,0.00166415,0.001163102,0.0008269626,0.0006298703,0.002569533],"category_scores_gemma":[0.002671151,0.0003849124,0.002349929,0.003364306,0.0007336155,0.000643363,0.002208002,0.0009245233,0.0002455937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002064638,"about_ca_system_score_gemma":0.002285728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2227045,"about_ca_topic_score_gemma":0.2002005,"domain_scores_codex":[0.9987738,0.000370282,0.00007695258,0.0002456061,0.0001651175,0.0003682619],"domain_scores_gemma":[0.9973884,0.0005321136,0.0006519987,0.0004445115,0.0003959771,0.0005870063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005108108,0.00004783901,0.9970831,0.000008206658,0.0003302069,0.00007470392,0.0004679149,0.0005792427,0.0000820552,0.000111842,0.0001672587,0.0009965145],"study_design_scores_gemma":[0.000005361428,0.00003337501,0.9947209,0.000008460957,0.0001079461,0.00001576763,0.001272159,0.003582064,0.00003771377,0.0000630464,0.0001407023,0.00001235498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993892,0.00004472529,0.0001021636,0.00005139617,0.000002146566,0.000008879391,0.000247585,0.000003317556,0.0001505733],"genre_scores_gemma":[0.9995009,0.0000250209,0.00006719154,0.00001090075,0.000002218333,0.00001238241,0.0002623652,0.000002052654,0.000116887],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2227045,"threshold_uncertainty_score":0.4428164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01824288151201355,"score_gpt":0.3226492192140026,"score_spread":0.304406337701989,"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."}}