{"id":"W6939187088","doi":"10.6068/dp14ba8e08d1096","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, educational attainment, sex and age group | Variable: 25 to 54 years, Employment rate, Total, all education levels, Males, Immigrants, landed more than 5 to 10 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":"Memory, History, Trauma, Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Immigration; Unemployment; Census; Official statistics; Socioeconomic status; Descriptive statistics; Population; Ethnic group; Diversity (politics)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008116607,0.0005900091,0.0006919333,0.0001576074,0.0003565431,0.0004365297,0.000693666,0.0002131883,0.004047676],"category_scores_gemma":[0.00008259146,0.0006270114,3.621769e-7,0.0001417452,0.0002480355,0.0003709665,0.0007305449,0.0003625086,0.00001705359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003950922,"about_ca_system_score_gemma":0.003625717,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9999037,"about_ca_topic_score_gemma":0.9999102,"domain_scores_codex":[0.9965402,0.0004230438,0.0005691307,0.001035368,0.0007943966,0.0006378866],"domain_scores_gemma":[0.9972739,0.0004004443,0.0003890993,0.001106621,0.0001204742,0.0007095144],"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":[0.0001163695,0.0001030367,0.001243223,0.0003449448,0.0002297294,0.0000422339,0.0001808857,0.00000236185,0.000002920302,0.0002513505,0.9974003,0.00008267963],"study_design_scores_gemma":[0.0005829742,0.00008140121,0.01865055,0.00004782972,0.0002444375,0.00001769985,0.0007960655,0.00006836581,8.956405e-9,7.239041e-7,0.9787885,0.0007214575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005480552,0.00470201,0.000002459427,0.00002003872,0.000683986,0.0008628531,0.9929386,0.00003281642,0.0002091701],"genre_scores_gemma":[0.0002048755,0.001125266,0.0001008355,0.0004765753,0.00016803,0.0000309655,0.9431368,0.0001212755,0.05463543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05442626,"threshold_uncertainty_score":0.9996181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03004686055508191,"score_gpt":0.2504126422758045,"score_spread":0.2203657817207226,"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."}}