{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002603019,0.00009106268,0.0002014276,0.0002283751,0.0002224455,0.00006216842,0.00004363894,0.00007642226,0.0001995858],"category_scores_gemma":[0.0004333548,0.00008319383,0.0000774395,0.0009562371,0.00006274413,0.0001567059,0.00001139653,0.00006052451,0.000002110522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003671561,"about_ca_system_score_gemma":0.00004866162,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01416039,"about_ca_topic_score_gemma":0.2906584,"domain_scores_codex":[0.9990565,0.0002111403,0.0001799239,0.0001575895,0.000280357,0.0001145254],"domain_scores_gemma":[0.9992684,0.0002439293,0.0001386023,0.0001066366,0.0001851243,0.00005738375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005013915,0.00006160154,0.761098,0.00001779065,0.0002009517,0.000001460647,0.156035,0.008697947,0.0002606746,0.07205327,0.000529536,0.0009937027],"study_design_scores_gemma":[0.0003025052,0.00002396981,0.9079366,0.00002306936,0.0002015721,2.81288e-7,0.02058795,0.06318976,0.00007826441,0.0007642943,0.006703901,0.0001878261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939438,0.0001185161,0.0005683255,0.003345177,0.00006926159,0.00009492155,0.00009405,0.00002657806,0.001739432],"genre_scores_gemma":[0.9927471,0.0002266995,0.000257268,0.0001878935,0.00004967813,0.000004486572,0.0001976763,0.00000459695,0.006324569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.276498,"threshold_uncertainty_score":0.9924044,"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."}}