{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002540738,0.00009395944,0.0002087403,0.0001447075,0.0002114618,0.00007851293,0.0005429558,0.00002850484,0.00001764283],"category_scores_gemma":[0.0005980972,0.00007473653,0.00008062773,0.0009620036,0.0001162671,0.0001547477,0.000955088,0.0004023341,4.340293e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319205,"about_ca_system_score_gemma":0.0001879273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002062385,"about_ca_topic_score_gemma":0.0002500743,"domain_scores_codex":[0.9982324,0.000581275,0.0002134119,0.0004875472,0.0002275758,0.0002578523],"domain_scores_gemma":[0.9986925,0.0003670007,0.0000690392,0.0007407366,0.00008365348,0.0000470961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006821024,0.00005359527,0.952753,0.00003706461,0.00003676393,0.00001772995,0.002596452,0.006657087,0.000001521313,0.004029939,0.00000633965,0.03380365],"study_design_scores_gemma":[0.0001635118,0.00003962906,0.868318,0.000001111433,0.00003344535,0.000003429576,0.002903786,0.07990394,0.000001846603,0.04748935,0.001031692,0.0001102466],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985962,0.0008074164,0.0008383744,0.01206756,0.00003674422,0.0001398611,0.000001491764,0.00007431993,0.00007225431],"genre_scores_gemma":[0.9995393,0.00005906117,0.0002186741,0.0000717661,0.00001255464,0.00001885823,8.552435e-7,0.00000355032,0.00007542041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08443502,"threshold_uncertainty_score":0.3047667,"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."}}