{"id":"W1519128423","doi":"10.1111/caje.12142","title":"Technological change, occupational tasks and declining immigrant outcomes: Implications for earnings and income inequality in Canada","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; Dalhousie University; Canada Foundation for Innovation","keywords":"Earnings; Immigration; Cohort; Inequality; Demographic economics; Economics; Labour economics; Wage; Cohort effect; Task (project management); Medicine; Political science; Accounting","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.000860234,0.0002795969,0.0003832501,0.001951275,0.004091968,0.001789806,0.0009567013,0.0004662266,0.004088541],"category_scores_gemma":[0.00309531,0.0001063641,0.0004199803,0.00393767,0.001333395,0.0004811816,0.00137761,0.0008344651,0.0001705972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02086055,"about_ca_system_score_gemma":0.02953927,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951566,"about_ca_topic_score_gemma":0.996143,"domain_scores_codex":[0.999325,0.00003392138,0.00002112985,0.00007476108,0.0001590493,0.000386181],"domain_scores_gemma":[0.9978137,0.0001873739,0.0004260333,0.00007173081,0.0008580769,0.00064305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001550159,0.00004573186,0.9771971,0.00003387121,0.0000442806,0.000179935,0.002419798,0.0006689354,0.0002089952,0.00319621,0.001902885,0.01394723],"study_design_scores_gemma":[0.000004848857,0.000009572067,0.9951569,0.00003602384,0.00001421111,0.00002123684,0.002738274,0.0004381146,0.00005742458,0.0002933364,0.001219725,0.00001014707],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990837,0.0008417222,0.0001015859,0.0016042,0.00001603517,0.00001129068,0.001641027,0.000006254146,0.004940952],"genre_scores_gemma":[0.9981127,0.0003135934,0.00005192619,0.000062475,0.000004760802,0.000003347088,0.0004996644,0.000002425061,0.0009491232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02086055,"threshold_uncertainty_score":0.1513546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2548797826718168,"score_gpt":0.2771455618794756,"score_spread":0.02226577920765882,"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."}}