{"id":"W2520265707","doi":"","title":"The Ethno-Lingual Composition of the Russian Federation and Canada: AComparative Analysis","year":2016,"lang":"en","type":"article","venue":"Global media journal Australia","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multiculturalism; Ethnic group; Russian federation; Context (archaeology); Composition (language); Official language; Language policy; Russian language; Immigration; Linguistics; Political science; Ethnic composition; Multilingualism; Sociology; Geography; Law; Regional science","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":[0.001294785,0.0002958133,0.00042168,0.005428637,0.0083268,0.003410367,0.0009341666,0.0003674547,0.002463914],"category_scores_gemma":[0.002477344,0.0002216988,0.0003014093,0.00867834,0.002750481,0.0008374912,0.003060702,0.0004909114,0.0001631218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01984686,"about_ca_system_score_gemma":0.027016,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9778848,"about_ca_topic_score_gemma":0.9907637,"domain_scores_codex":[0.9980909,0.0003172361,0.00006977385,0.0001614832,0.0005149659,0.000845646],"domain_scores_gemma":[0.9982908,0.0003497512,0.0002200666,0.00005459471,0.0007835804,0.000301201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001468982,0.00007620745,0.6181269,0.0002040504,0.00008324884,0.0007849638,0.3316542,0.000138447,0.001591083,0.005845589,0.001768771,0.03957971],"study_design_scores_gemma":[0.000002172812,0.00002040249,0.6287616,0.0001229333,0.00002691729,0.0001841183,0.3626792,0.000153002,0.0002002168,0.0001093067,0.007717962,0.00002210181],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898993,0.0006859473,0.0001018502,0.0001983401,0.000007231527,0.0000325653,0.0003811956,0.000003511844,0.008690194],"genre_scores_gemma":[0.996998,0.0008187451,0.0001716449,0.00007221087,0.000004482442,0.00002147835,0.0003599518,0.000005515675,0.001547916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02211517,"threshold_uncertainty_score":0.1439998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07152971718920131,"score_gpt":0.4396386205839444,"score_spread":0.3681089033947431,"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."}}