{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002182398,0.0006448937,0.001181235,0.0001603197,0.0002510243,0.0004125612,0.001100786,0.0003726203,0.0007335699],"category_scores_gemma":[0.0002734034,0.0005359479,2.906606e-7,0.0006681905,0.0004745186,0.0004080671,0.001447856,0.0004607439,0.000002095952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008030859,"about_ca_system_score_gemma":0.003061761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9999127,"about_ca_topic_score_gemma":0.9998505,"domain_scores_codex":[0.9947769,0.0009609977,0.0009055496,0.001218619,0.001410679,0.000727242],"domain_scores_gemma":[0.9946343,0.002335004,0.0007695268,0.001490448,0.0001085377,0.0006622189],"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":[0.000316185,0.00004166051,0.00475315,0.0002506543,0.0001968803,0.0002899767,0.00001603769,0.000007296214,0.000004291315,0.0001932841,0.9937478,0.0001828091],"study_design_scores_gemma":[0.001409574,0.0001105522,0.007268354,0.00004680251,0.0001786326,0.00004823747,0.0002388023,0.0008042077,2.727359e-8,0.00000144475,0.9892433,0.0006500427],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004869166,0.008096998,0.000005830758,0.000003123206,0.0002714226,0.0003935827,0.9905757,0.00002791806,0.0001385376],"genre_scores_gemma":[0.0001740645,0.01445765,0.0001036328,0.0002664742,0.00005366342,0.000004922299,0.9703053,0.0001107844,0.01452352],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02027038,"threshold_uncertainty_score":0.9997092,"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."}}