{"id":"W2017061997","doi":"10.1007/s12134-010-0160-6","title":"Color by Numbers: Minority Earnings in Canada 1995–2005","year":2010,"lang":"en","type":"article","venue":"Journal of International Migration and Integration / Revue de l integration et de la migration internationale","topic":"Migration, Ethnicity, and Economy","field":"Social Sciences","cited_by":60,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Earnings; Census; Demographic economics; Ethnic group; Population; Convergence (economics); Demography; Geography; Political science; Economics; Sociology; Economic growth","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00348578,0.0003003155,0.0003605496,0.0005972442,0.0002493272,0.0005562466,0.0005319975,0.0002871552,0.001137438],"category_scores_gemma":[0.003443308,0.0003067731,0.0001557568,0.0003232734,0.0002039491,0.002075516,0.00003135052,0.001151319,0.00001073709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002046723,"about_ca_system_score_gemma":0.00282831,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6150025,"about_ca_topic_score_gemma":0.9918092,"domain_scores_codex":[0.9964302,0.0007284863,0.00136511,0.0003486266,0.0007740535,0.0003535103],"domain_scores_gemma":[0.9960448,0.0009930785,0.001161248,0.0001632706,0.001342796,0.0002947833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004279439,0.0007305274,0.5142883,0.0000303848,0.0001843475,0.00003317088,0.04393398,0.001025617,0.07436018,0.2238762,0.134228,0.006881353],"study_design_scores_gemma":[0.002172785,0.0002414383,0.2306681,0.0003197156,0.00005612185,0.0003480242,0.01733902,0.03856619,0.009229793,0.004496431,0.6957772,0.0007852534],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9391518,0.0001242157,0.0172085,0.02295442,0.001575511,0.0003293596,0.00007303942,0.00003585331,0.01854728],"genre_scores_gemma":[0.9856252,0.002135599,0.004014588,0.001905438,0.000882401,0.00006267933,0.0002394155,0.0000241525,0.005110547],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5615492,"threshold_uncertainty_score":0.9999384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0105839995414713,"score_gpt":0.2848416567313035,"score_spread":0.2742576571898321,"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."}}