{"id":"W4401520293","doi":"10.1007/s10758-024-09767-w","title":"Populations Digitally Excluded from Education: Issues, Factors, Contributions and Actions for Policy, Practice and Research in a Post-Pandemic Era","year":2024,"lang":"en","type":"article","venue":"Technology Knowledge and Learning","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"","keywords":"Pandemic; Science education; Coronavirus disease 2019 (COVID-19); Educational technology; 2019-20 coronavirus outbreak; Engineering ethics; Political science; Sociology; Economic growth; Pedagogy; Medicine; Virology; Economics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01289107,0.0002459614,0.0004780744,0.001631175,0.007669895,0.01298027,0.001524462,0.003427445,0.007997181],"category_scores_gemma":[0.0194741,0.0002136016,0.0003944778,0.001444372,0.01797882,0.009551808,0.01369746,0.006115736,0.0003586723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009944452,"about_ca_system_score_gemma":0.02127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01733374,"about_ca_topic_score_gemma":0.02331585,"domain_scores_codex":[0.9894397,0.006534472,0.0003100803,0.0004230445,0.001072329,0.002220452],"domain_scores_gemma":[0.9836283,0.009135535,0.001864968,0.0004370657,0.001384255,0.003549991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001516791,0.0006010502,0.07660022,0.00130118,0.00005302694,0.002585236,0.3611816,0.0006563849,0.0008696164,0.3976439,0.02253361,0.1358224],"study_design_scores_gemma":[0.00001703593,0.0001715178,0.02908183,0.003383584,0.00002705867,0.0005103036,0.800676,0.000257071,0.0003494515,0.07431995,0.09113974,0.00006637956],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.3410835,0.007723111,0.002424281,0.5850204,0.0007883772,0.0001666576,0.0001679265,0.00002831626,0.06259754],"genre_scores_gemma":[0.9778977,0.005148546,0.001085758,0.0127309,0.0002166701,0.0001599355,0.00004722206,0.000009937552,0.002703235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01733374,"threshold_uncertainty_score":0.07215238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1555236297127744,"score_gpt":0.5604688485563607,"score_spread":0.4049452188435863,"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."}}