{"id":"W1855269821","doi":"10.25336/p6np62","title":"Immigrant Language Proficiency, Earnings, and Language Policies","year":2009,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Microdata (statistics); Earnings; Immigration; Language proficiency; Demographic economics; Language policy; Government (linguistics); Quantile regression; Political science; Economics; Census; Psychology; Linguistics; Sociology; Demography; Accounting; Population; Econometrics; Mathematics education","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007459458,0.0001264708,0.0001699555,0.0006844613,0.001560547,0.001240547,0.0003733182,0.0002795596,0.003316969],"category_scores_gemma":[0.003769299,0.0000491469,0.0001485929,0.0009079371,0.0009519735,0.0003751824,0.0008558766,0.0005052359,0.0001532143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005430991,"about_ca_system_score_gemma":0.008216285,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8128172,"about_ca_topic_score_gemma":0.8944256,"domain_scores_codex":[0.9994057,0.00007817188,0.00001629721,0.00003406467,0.0001383499,0.000327431],"domain_scores_gemma":[0.9980446,0.0002846398,0.0005999574,0.0000430279,0.0004583541,0.0005694411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008304562,0.00008448455,0.9659323,0.00002625579,0.0000353373,0.0003156796,0.001776858,0.001063476,0.0002509466,0.00786687,0.002061678,0.02050309],"study_design_scores_gemma":[0.000004738375,0.00003236856,0.9904338,0.00004073602,0.00001599471,0.00004130679,0.004210531,0.000344594,0.0001084907,0.000660707,0.004097139,0.000009629222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985089,0.0005744956,0.00007653741,0.002779293,0.00001149778,0.000006635872,0.0003118386,0.000006490831,0.01114423],"genre_scores_gemma":[0.9969907,0.0004445271,0.00005062409,0.0001434381,0.00001326885,0.000002527171,0.0001692603,0.000001447935,0.002184162],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1871828,"threshold_uncertainty_score":0.3765703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193052672901451,"score_gpt":0.3437461501307948,"score_spread":0.3244408828406498,"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."}}