{"id":"W2064966236","doi":"10.1016/j.ehb.2012.10.003","title":"Stature, migration and human welfare in South China, 1850–1930","year":2012,"lang":"en","type":"article","venue":"Economics & Human Biology","topic":"Historical Economic and Social Studies","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Social Science Research Council","keywords":"China; Life expectancy; Demography; Socioeconomic status; Falling (accident); Immigration; Geography; Demographic economics; Welfare; Population; Development economics; Economics; Sociology; Psychology","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":[0.0005071114,0.0002102687,0.0002019946,0.001474806,0.0008972376,0.0004385492,0.0003799631,0.0002500039,0.001371199],"category_scores_gemma":[0.0007698506,0.0001377226,0.0002670407,0.002611048,0.001181122,0.0005444,0.0005965971,0.000355203,0.000137138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679892,"about_ca_system_score_gemma":0.00124798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1206133,"about_ca_topic_score_gemma":0.2366476,"domain_scores_codex":[0.9998592,0.00002164206,0.00001191104,0.00003102728,0.00001655956,0.0000595869],"domain_scores_gemma":[0.9996539,0.00003480511,0.0001108818,0.00002496466,0.00007225039,0.0001032256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001386077,0.00007047244,0.9715456,0.00006526455,0.0001292869,0.0003022627,0.00560631,0.001040174,0.0006908253,0.00312682,0.0007340618,0.01655014],"study_design_scores_gemma":[0.000002518149,0.00002148929,0.9975774,0.000008560437,0.00001650592,0.00002471686,0.0006996961,0.0002573614,0.00004063103,0.0002441321,0.001102697,0.000004241686],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985336,0.0003720227,0.00003620203,0.0001482069,0.000006702592,0.000001535637,0.0001618288,0.00000161138,0.0007382879],"genre_scores_gemma":[0.999162,0.0001618467,0.00002462118,0.00001458896,0.00001012073,0.000001857639,0.0001329293,6.149091e-7,0.0004914352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1206133,"threshold_uncertainty_score":0.2398225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02959342178226624,"score_gpt":0.2340623574664604,"score_spread":0.2044689356841942,"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."}}