{"id":"W3122948869","doi":"10.3386/w21591","title":"Immigrant Employment and Earnings Growth in Canada and the U.S.: Evidence from Longitudinal Data","year":2015,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Fonds de Recherche du Québec-Société et Culture; National Institute of Child Health and Human Development; Canadian Institutes of Health Research; Russell Sage Foundation; Columbia Population Research Center; Sage Foundation; National Science Foundation","keywords":"Immigration; Wage growth; Earnings growth; Earnings; Longitudinal data; Demographic economics; Wage; Economics; Longitudinal study; Labour economics; Demography; Geography; Sociology; Medicine","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":[0.001907731,0.0003670093,0.0004594868,0.002361069,0.002825922,0.001881744,0.001146986,0.0004574742,0.002157459],"category_scores_gemma":[0.008035183,0.0003041542,0.0007258257,0.007930499,0.0008962938,0.0007306367,0.00188182,0.001162171,0.0004119505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01172744,"about_ca_system_score_gemma":0.02335196,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9914736,"about_ca_topic_score_gemma":0.9936592,"domain_scores_codex":[0.9990063,0.0001451743,0.00007348373,0.0001552433,0.0002912203,0.0003286084],"domain_scores_gemma":[0.9918457,0.001055483,0.002152873,0.0004649212,0.003158825,0.001322199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004495366,0.00002716245,0.9911528,0.00002375402,0.0001021396,0.00004734754,0.0005829587,0.00042369,0.00002286965,0.0003463201,0.001879299,0.00534675],"study_design_scores_gemma":[0.000008246593,0.00001306967,0.9941398,0.0001006471,0.00006108867,0.00002648324,0.001516347,0.0009700681,0.00004762579,0.0001330324,0.002967643,0.00001594248],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9782164,0.002635367,0.0002843997,0.001623355,0.00004087959,0.00001954001,0.01457887,0.00002338135,0.002577889],"genre_scores_gemma":[0.9805946,0.002683881,0.0004205908,0.0002138211,0.00001899506,0.00002204809,0.01434503,0.00001238756,0.001688786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01172744,"threshold_uncertainty_score":0.08508891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3534582895915804,"score_gpt":0.4795756779697236,"score_spread":0.1261173883781432,"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."}}