{"id":"W2108764314","doi":"10.25336/p6c326","title":"Job matching for Chinese and Asian Indian immigrants in Canada","year":2013,"lang":"en","type":"article","venue":"Canadian Studies in Population","topic":"Migration and Labor Dynamics","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; University of Toronto","funders":"","keywords":"Immigration; Earnings; Demographic economics; Asian Indian; Matching (statistics); Earnings growth; Asian americans; Multivariate analysis; Political science; Ethnic group; Economics; Medicine","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":[0.0001839452,0.00006553366,0.0001166206,0.0001540377,0.0002291018,0.00002786449,0.00005411312,0.00003958089,0.00001918245],"category_scores_gemma":[0.0002125397,0.00006575802,0.000008974643,0.0002584515,0.00003331399,0.0001595514,0.000006414226,0.00005668198,9.567983e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332514,"about_ca_system_score_gemma":0.0005033896,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9998614,"about_ca_topic_score_gemma":0.9999998,"domain_scores_codex":[0.9993386,0.00004648908,0.0001625011,0.0001156146,0.00008822553,0.0002485959],"domain_scores_gemma":[0.9996901,0.00004850319,0.0000349458,0.00005044422,0.00004281155,0.0001331812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[8.203918e-7,0.000001074221,0.9447223,0.00001156878,0.00000409575,0.000002757202,0.04463687,0.00005917262,5.240669e-7,0.002646785,0.0004804475,0.00743356],"study_design_scores_gemma":[0.0001301687,0.00000233831,0.9414843,0.00002074808,0.000001145387,2.115051e-7,0.05296759,0.0003919166,3.36116e-8,0.004168387,0.0007523017,0.00008089402],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956438,0.0001862065,0.000002624975,0.002674849,0.0002612908,0.0003996163,0.00001829096,0.00000466598,0.0008086764],"genre_scores_gemma":[0.999147,0.000071487,0.00008574031,0.0004016366,0.00004713221,0.00005136143,0.00001627457,0.000005324693,0.0001740668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00833072,"threshold_uncertainty_score":0.3484477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01675508073662238,"score_gpt":0.3028241547923848,"score_spread":0.2860690740557624,"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."}}