{"id":"W7095851130","doi":"","title":"NBER WORKING PAPER SERIES IMMIGRANT EMPLOYMENT AND EARNINGS GROWTH IN CANADA AND THE U.S.: EVIDENCE FROM LONGITUDINAL DATA","year":2015,"lang":"en","type":"article","venue":"","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Earnings; Immigration; Series (stratigraphy); Longitudinal data; Time series; Earnings growth","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003988361,0.0005698845,0.001234787,0.003707098,0.002255601,0.003276544,0.001570955,0.0007948247,0.0113532],"category_scores_gemma":[0.0156665,0.0004519607,0.001012648,0.01228109,0.0007963827,0.00107637,0.00130135,0.001375737,0.003018573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007945665,"about_ca_system_score_gemma":0.02679029,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9773799,"about_ca_topic_score_gemma":0.9787846,"domain_scores_codex":[0.9983855,0.0001863773,0.0001503673,0.0002042551,0.0007576582,0.0003158989],"domain_scores_gemma":[0.979357,0.003682108,0.003724172,0.001079938,0.01028145,0.001875369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001585908,0.0001101833,0.7290876,0.0002732487,0.0005064643,0.0001565933,0.0004989679,0.001235929,0.00007951512,0.002624453,0.2412956,0.02397289],"study_design_scores_gemma":[0.0001230795,0.00002826516,0.936985,0.0005590256,0.0003482784,0.0000467566,0.001331104,0.001513673,0.0001707616,0.0007459591,0.05809867,0.00004948414],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2796993,0.02273091,0.001551556,0.02131781,0.0007951044,0.0002292334,0.6405836,0.0003164827,0.03277595],"genre_scores_gemma":[0.4737659,0.02779465,0.00212818,0.00231449,0.0005229469,0.0003425066,0.4403135,0.000277315,0.05254053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02262014,"threshold_uncertainty_score":0.05765015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060012442199322,"score_gpt":0.2882076421042344,"score_spread":0.1822063978843022,"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."}}