{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004249722,0.00006798923,0.00006780309,0.0001715823,0.0001292367,0.0001004897,0.00006955447,0.00006308626,0.00005851355],"category_scores_gemma":[0.0001139729,0.00005698074,0.00001383006,0.0001632021,0.0000969777,0.0005801137,0.0000201395,0.00005230003,0.000008309084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001463201,"about_ca_system_score_gemma":0.0001298518,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009472538,"about_ca_topic_score_gemma":0.0453533,"domain_scores_codex":[0.9991679,0.0001515657,0.0001898637,0.0001618704,0.0002319257,0.00009685914],"domain_scores_gemma":[0.9994142,0.0002024436,0.0001015803,0.00005260513,0.0001781761,0.00005101294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002038219,0.00006219058,0.7115005,0.000004773754,0.000008266989,2.919175e-7,0.03032299,0.000007274372,0.0002767777,0.1449509,0.0006916672,0.1121539],"study_design_scores_gemma":[0.0005364008,0.00003115802,0.8596873,0.0001964118,0.000007076232,0.000001937902,0.01649092,0.0006070319,0.000126923,0.003216118,0.1188383,0.0002604118],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853957,0.0000641093,0.002012492,0.009255615,0.000289136,0.000143134,0.00001477531,0.00003538213,0.002789714],"genre_scores_gemma":[0.9854584,0.0002973224,0.0001644863,0.0001448763,0.0001046189,0.00003537698,0.00001263928,0.000004746361,0.01377756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1481867,"threshold_uncertainty_score":0.9720665,"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."}}