{"id":"W3196817970","doi":"10.33137/ijidi.v5i3.36159","title":"Promoting Linguistic Diversity and Inclusion","year":2021,"lang":"en","type":"article","venue":"The International Journal of Information Diversity & Inclusion (IJIDI)","topic":"Wikis in Education and Collaboration","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Machine translation; Literacy; Diversity (politics); Computer science; Information literacy; Inclusion (mineral); Artificial intelligence; Mathematics education; Pedagogy; World Wide Web; Sociology; Psychology; Social science","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","open_science"],"consensus_categories":[],"category_scores_codex":[0.002267368,0.00008085449,0.000110841,0.0001746153,0.0150965,0.0001652244,0.0008911604,0.00007144811,0.000243845],"category_scores_gemma":[0.002170537,0.00006825036,0.00007290005,0.0002922597,0.0001378844,0.001455406,0.03466027,0.000212107,0.00001319047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004324755,"about_ca_system_score_gemma":0.000458808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004392254,"about_ca_topic_score_gemma":0.0002467543,"domain_scores_codex":[0.9974981,0.0001877815,0.0003818062,0.00007076663,0.001739733,0.0001218221],"domain_scores_gemma":[0.9958903,0.0002012395,0.0006020413,0.00009336777,0.003104826,0.0001082457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001040841,0.00009886746,0.01265858,0.00000947998,0.00007529445,0.00001852313,0.942224,0.00006575501,0.0001351966,0.01871682,0.00277808,0.02311526],"study_design_scores_gemma":[0.003240433,0.0001708338,0.03366428,0.0004280808,0.0002116569,0.0001668081,0.1266075,0.002220573,0.002528334,0.0797985,0.7503695,0.000593444],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9518861,0.000147943,0.001027354,0.03321048,0.002706734,0.0001264652,0.000009054336,0.00002574968,0.01086017],"genre_scores_gemma":[0.995942,0.000429742,0.0003915019,0.002494771,0.0004802276,3.817829e-7,0.00001160882,0.000002170234,0.000247604],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8156165,"threshold_uncertainty_score":0.9861857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0170181528171583,"score_gpt":0.2918290807704478,"score_spread":0.2748109279532895,"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."}}