{"id":"W2737876540","doi":"10.1111/imig.12363","title":"Earning Gaps for Chinese Immigrants in Canada and the United States","year":2017,"lang":"en","type":"article","venue":"International Migration","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; University of Victoria","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Immigration; Earnings; Immigration policy; Context (archaeology); Demographic economics; Multiculturalism; Political science; Family reunification; Development economics; Economics; Geography; Law","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0004280218,0.0002030425,0.0003278933,0.00174866,0.003425184,0.00142843,0.0005193593,0.000254474,0.003770211],"category_scores_gemma":[0.001667436,0.00007283017,0.0002827985,0.00317583,0.0006158236,0.0003953064,0.001372949,0.0005757534,0.0002361063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0105049,"about_ca_system_score_gemma":0.01832981,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9831797,"about_ca_topic_score_gemma":0.9918394,"domain_scores_codex":[0.999504,0.00002581407,0.00001694935,0.00003183903,0.0001054025,0.0003160411],"domain_scores_gemma":[0.9987082,0.00008197789,0.0001726854,0.00002661651,0.0005283458,0.0004821639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000176692,0.00006523324,0.9721895,0.00002580528,0.00003381178,0.0001996135,0.006352643,0.0002695876,0.0001500035,0.00172977,0.0034008,0.01540655],"study_design_scores_gemma":[0.000005975705,0.00002107092,0.9744752,0.0000337287,0.00001742111,0.00004438436,0.0223228,0.0003526573,0.00009752485,0.0001465652,0.002466652,0.00001605059],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956567,0.0001649787,0.00001382297,0.0002708623,0.00000820097,0.000004918571,0.00082847,0.000002626119,0.003049419],"genre_scores_gemma":[0.9978296,0.0001431559,0.0000247525,0.0000487266,0.000002437583,0.00000346166,0.0006727112,0.000001715404,0.001273467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01682031,"threshold_uncertainty_score":0.07621872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019386011743476,"score_gpt":0.2998635598615858,"score_spread":0.289669699744151,"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."}}