{"id":"W2018643698","doi":"10.1111/j.0038-4941.2005.00343.x","title":"Training and the Earnings of Immigrant Males: Evidence from the <i>Canadian Workplace and Employee Survey</i><sup>*</sup>","year":2005,"lang":"en","type":"article","venue":"Social Science Quarterly","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Earnings; Disadvantaged; Immigration; Disadvantage; Wage; Demographic economics; Training (meteorology); Panel data; Labour economics; Economics; Psychology; Political science; Econometrics; Economic growth; Geography; Finance","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001422514,0.0002237645,0.0002395779,0.001199995,0.001290004,0.0007698588,0.0007939152,0.000423889,0.002439661],"category_scores_gemma":[0.004617738,0.0001463573,0.0003083675,0.002433519,0.0007462071,0.0002595386,0.0006416028,0.0005024406,0.0001763727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002809615,"about_ca_system_score_gemma":0.005311593,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9549996,"about_ca_topic_score_gemma":0.9738135,"domain_scores_codex":[0.9994237,0.00008968203,0.00003482678,0.0000603645,0.0002100752,0.0001813627],"domain_scores_gemma":[0.9967119,0.0006260304,0.001084317,0.0001511377,0.0009620289,0.0004646463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001013414,0.00003115795,0.9913941,0.0000313996,0.00006220538,0.00004143234,0.0005199939,0.00009863692,0.00004443066,0.0002325247,0.001020944,0.006421758],"study_design_scores_gemma":[0.000004026031,0.00001135039,0.9983429,0.00002882288,0.00002651689,0.00001328489,0.0006758388,0.0000729649,0.00003129771,0.00003065866,0.000758272,0.000004037338],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924456,0.001363558,0.00005851164,0.000793772,0.00001333167,0.00000873609,0.003081271,0.000002939734,0.002232225],"genre_scores_gemma":[0.995894,0.00121968,0.00005972983,0.0001333778,0.00001367308,0.000005208837,0.001852626,0.000001771133,0.0008199878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04500037,"threshold_uncertainty_score":0.09053075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03988032807989451,"score_gpt":0.2944147251018273,"score_spread":0.2545343970219328,"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."}}