{"id":"W6957833295","doi":"10.6068/dp14ba8c30aef80","title":"Trend 2006 - 2013. Statistics Canada. CANSIM: Ethnic Diversity and Immigration - Labor Market and Income | Country: Canada | Table: Labour force survey estimates (LFS), by immigrant status, country of birth, sex and age group | Variable: 15 years and over, Europe, Unemployment rate, Both sexes, Immigrants, landed 5 or less years earlier | Units: , 2006-2013. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-094.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Energy Law and Policy","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Unemployment; Official statistics; Census; Descriptive statistics; Socioeconomic status; Population; Ethnic group; Diversity (politics); Summary statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002858682,0.002586483,0.003017858,0.007947304,0.003470718,0.004972294,0.005855013,0.001464015,0.09640212],"category_scores_gemma":[0.01985716,0.002038426,0.002581379,0.03826969,0.0006059238,0.002403439,0.00255756,0.003316228,0.05275161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05436384,"about_ca_system_score_gemma":0.1474546,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951336,"about_ca_topic_score_gemma":0.9933829,"domain_scores_codex":[0.9950353,0.0003779131,0.0005738356,0.0005755843,0.002268505,0.001168834],"domain_scores_gemma":[0.9600188,0.001280841,0.001148428,0.001061656,0.03455377,0.001936453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003349609,0.000008796908,0.001277596,0.0002616598,0.00002641773,0.000007142804,0.00002669408,0.0000959352,0.0000102387,0.0003200928,0.9959515,0.001980328],"study_design_scores_gemma":[0.0002486566,0.00002028163,0.03922082,0.001297425,0.0001091419,0.00003509147,0.0006468704,0.0005032917,0.0002157475,0.0007824867,0.9568026,0.0001177143],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007225306,0.00005922524,0.00002902088,0.0001469859,0.00004136584,0.00002493694,0.9985274,0.00006136882,0.001037446],"genre_scores_gemma":[0.0009938405,0.0003410261,0.0005234787,0.0002482262,0.00002776204,0.0002174371,0.9911743,0.000150028,0.006323838],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09640212,"threshold_uncertainty_score":0.394439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03450073319740557,"score_gpt":0.2771382304715436,"score_spread":0.242637497274138,"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."}}