{"id":"W3200344356","doi":"10.21432/cjlt28150","title":"Teaching and Learning with Technology During the COVID-19 Pandemic: Highlighting the Need for Micro-Meso-Macro Alignments","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Learning and Technology","topic":"Educational Innovations and Technology","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Conceptualization; Coronavirus disease 2019 (COVID-19); Theme (computing); Pandemic; Macro; Sociology; Pedagogy; Psychology; Mathematics education; Computer science; Medicine; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0009862721,0.0001332195,0.0002009662,0.0007685983,0.002293556,0.0001521776,0.0005400125,0.000203669,0.000004249653],"category_scores_gemma":[0.001663364,0.00008714532,0.00002615165,0.0009591479,0.0004541341,0.0001192857,0.0001261328,0.00168734,5.160678e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001311229,"about_ca_system_score_gemma":0.0009318524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002533788,"about_ca_topic_score_gemma":0.001076553,"domain_scores_codex":[0.9988672,0.0001306523,0.0002927799,0.000253219,0.00008675651,0.0003693548],"domain_scores_gemma":[0.9986224,0.0004176631,0.0003293063,0.0002154863,0.000286474,0.0001286656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008666299,0.0000170006,0.6594554,0.00004359804,0.0001935355,0.0001797675,0.002100497,0.0001937205,0.01128826,0.2754269,0.0002185451,0.05087417],"study_design_scores_gemma":[0.001972591,0.0006339815,0.0028382,0.0001554741,0.00007886902,0.03469542,0.03854037,0.0009792784,0.003390486,0.03458193,0.881632,0.0005014394],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8365716,0.002092728,0.01915149,0.1418116,0.0001054162,0.00009854256,0.000001001227,0.00009217622,0.00007534772],"genre_scores_gemma":[0.9840918,0.00006406041,0.01472901,0.0003700725,0.00004722257,0.00001581558,0.00000111303,0.00001303169,0.0006679296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8814134,"threshold_uncertainty_score":0.9990053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01382914202126517,"score_gpt":0.2610682803596689,"score_spread":0.2472391383384037,"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."}}