{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000272348,0.00006062948,0.00009477195,0.0001690855,0.0002971099,0.00002878057,0.00005408487,0.00004655624,0.00001478898],"category_scores_gemma":[0.0002904641,0.00005965167,0.00001279218,0.0003008358,0.00009050317,0.00009595858,0.000005585409,0.00006438684,0.000003073917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002368331,"about_ca_system_score_gemma":0.00006095223,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5192605,"about_ca_topic_score_gemma":0.9767367,"domain_scores_codex":[0.9993735,0.00006262736,0.0001160939,0.0001117918,0.0001102631,0.000225708],"domain_scores_gemma":[0.9997485,0.00002123353,0.00003643245,0.00006293747,0.0000321574,0.00009868317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.000002579037,0.000008285434,0.1726865,0.000008216244,0.000005832333,0.00001761652,0.745669,0.00004478021,0.00006104379,0.06255855,0.0005805431,0.01835698],"study_design_scores_gemma":[0.0001237428,0.00002065238,0.7502825,0.0000277779,0.000005324704,9.272474e-7,0.2442012,0.0002803356,0.000003575131,0.0004385644,0.004481276,0.0001340626],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908963,0.001593567,0.000003093957,0.002304362,0.0000984947,0.0001493146,0.000006958829,0.00002834813,0.004919509],"genre_scores_gemma":[0.9978814,0.0002207512,0.00005441293,0.0005650663,0.00007345524,0.000003850018,0.00001401785,0.000003113465,0.001183962],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5775959,"threshold_uncertainty_score":0.4839408,"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."}}