{"id":"W4220710338","doi":"10.3390/su14063340","title":"Accessibility Challenges in OER and MOOC: MLR Analysis Considering the Pandemic Years","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Erasmus+; European Commission; National Research Council Canada; University of Manitoba","keywords":"Terminology; Open educational resources; Inclusion (mineral); Computer science; Lifelong learning; Grey literature; Knowledge management; Coronavirus disease 2019 (COVID-19); Data science; Political science; World Wide Web; Sociology; Pedagogy; MEDLINE; Social science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.006455997,0.0003239021,0.0007431089,0.009526129,0.0009173069,0.005598971,0.0006347879,0.0008184548,0.004717588],"category_scores_gemma":[0.02840926,0.0001675398,0.000738138,0.009022454,0.001077963,0.007123944,0.003345254,0.001003833,0.0004168019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003148474,"about_ca_system_score_gemma":0.004288904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0135505,"about_ca_topic_score_gemma":0.01249188,"domain_scores_codex":[0.996276,0.0009996869,0.0004745208,0.0004412651,0.001325441,0.0004832158],"domain_scores_gemma":[0.9804174,0.009996521,0.002707394,0.0005568634,0.005711395,0.0006103395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0004321843,0.0002340463,0.1984012,0.01735564,0.0005118188,0.003789504,0.05817163,0.005363707,0.001922325,0.09634816,0.02255301,0.5949168],"study_design_scores_gemma":[0.00002584531,0.0004404116,0.3492856,0.0149364,0.0008669586,0.002365063,0.1822105,0.009855216,0.001976851,0.03082991,0.4070361,0.0001712196],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7003235,0.1135004,0.02683374,0.01674719,0.001323365,0.001003988,0.008298261,0.0001864948,0.131783],"genre_scores_gemma":[0.9676219,0.0228777,0.003795948,0.0005818459,0.0002629463,0.0002555275,0.001454838,0.00005070731,0.003098496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9993652,"threshold_uncertainty_score":0.03414303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02989915836297116,"score_gpt":0.3075899672955843,"score_spread":0.2776908089326132,"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."}}