{"id":"W2804962263","doi":"10.1111/imig.12458","title":"Do Immigrants Catch‐up with the Natives in Terms of Earnings? Evidence from Individual Level Data of Canada","year":2018,"lang":"en","type":"article","venue":"International Migration","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Children, Community and Social Services","funders":"","keywords":"Immigration; Earnings; Demographic economics; Economics; Political science; Geography; Archaeology; Accounting","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":[0.001366078,0.0002226905,0.0004089765,0.001604936,0.001989183,0.00128826,0.001048956,0.0003559008,0.002692062],"category_scores_gemma":[0.004715595,0.0002354265,0.0005002688,0.00491227,0.0008297343,0.0003646933,0.001087928,0.0005778577,0.0003276105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006117111,"about_ca_system_score_gemma":0.009248086,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9863105,"about_ca_topic_score_gemma":0.9911252,"domain_scores_codex":[0.9989499,0.0001469932,0.00006942949,0.0002031369,0.000256478,0.0003740719],"domain_scores_gemma":[0.9954808,0.000754923,0.00140081,0.0003024642,0.001391444,0.0006695841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006731476,0.00001622146,0.9951231,0.00001602086,0.00005258633,0.00005433316,0.001068534,0.0001628285,0.00005980855,0.0001805277,0.0005649041,0.0026339],"study_design_scores_gemma":[0.000003887736,0.0000096601,0.9968463,0.00002096131,0.0000179242,0.00001651872,0.001935524,0.000280267,0.00003859572,0.00003433685,0.0007883863,0.000007675431],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943718,0.0002922955,0.0001035715,0.0001728378,0.000005515298,0.00001066275,0.003657199,0.000004755808,0.001381311],"genre_scores_gemma":[0.995286,0.0002915265,0.0001006767,0.00003955554,0.000002850208,0.000006770616,0.003221576,0.000003466195,0.001047497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01368952,"threshold_uncertainty_score":0.04438293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08672806722135595,"score_gpt":0.3432815889492233,"score_spread":0.2565535217278674,"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."}}