{"id":"W2566401869","doi":"10.1111/imig.12302","title":"STEM Education and STEM Work: Nativity Inequalities in Occupations and Earnings","year":2016,"lang":"en","type":"article","venue":"International Migration","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Immigration; Receipt; Earnings; Demographic economics; Inequality; Work (physics); American Community Survey; Political science; Demography; Sociology; Population; Economics; Census","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":[0.0008018092,0.000107172,0.0002087658,0.0009387817,0.0010137,0.001341336,0.0004160366,0.0003091001,0.005287936],"category_scores_gemma":[0.002362397,0.00007427769,0.0002402492,0.001577287,0.0008479394,0.000704559,0.001410879,0.0005236876,0.0003216324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006798286,"about_ca_system_score_gemma":0.001008566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07797423,"about_ca_topic_score_gemma":0.1685797,"domain_scores_codex":[0.9995008,0.0001013103,0.00002806722,0.00005904746,0.00007760311,0.0002332223],"domain_scores_gemma":[0.9982516,0.0003529275,0.0006451594,0.00006343663,0.0001527386,0.0005340456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004491364,0.00004864959,0.9914008,0.00001410247,0.00002547482,0.00006277253,0.0008173476,0.00009817052,0.000077401,0.002632657,0.0003070845,0.004470615],"study_design_scores_gemma":[0.000002020316,0.00002114417,0.9953516,0.00004699251,0.00001059351,0.0000311451,0.003026514,0.000197747,0.00003398477,0.000609196,0.0006650389,0.000004020595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951805,0.0006387978,0.00007380502,0.0006265917,0.00001031707,0.000002765497,0.0002121199,0.000001357149,0.003253786],"genre_scores_gemma":[0.9991124,0.0002305486,0.00001767717,0.00003645329,0.000008441751,0.00000181813,0.0000913158,7.262833e-7,0.0005005483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07797423,"threshold_uncertainty_score":0.1550407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059342997953778,"score_gpt":0.3237326343088222,"score_spread":0.2931392043292844,"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."}}