{"id":"W3175816043","doi":"10.14434/jotlt.v10i1.31565","title":"\"Kaikille okei\"- Everyone alright? Shifting topics and practices in language students' chat during the global covid-19 pandemic","year":2021,"lang":"en","type":"article","venue":"Journal of Teaching and Learning with Technology","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Sociology; Pedagogy; Medical education; Mathematics education; Computer science; Psychology; Medicine; Virology","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":[0.006427335,0.0003600687,0.0003085883,0.0007109573,0.01119073,0.006401892,0.001223403,0.002474794,0.003597101],"category_scores_gemma":[0.01309563,0.0002644786,0.0002480401,0.0004041198,0.007209754,0.006203082,0.007000851,0.003608779,0.0009418502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001403237,"about_ca_system_score_gemma":0.001783068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002968926,"about_ca_topic_score_gemma":0.007965812,"domain_scores_codex":[0.995836,0.002919307,0.00008575051,0.0003004472,0.0003154051,0.0005430863],"domain_scores_gemma":[0.9935238,0.00383373,0.0004931607,0.0002893932,0.0003735638,0.001486254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00008674822,0.0001016983,0.007554241,0.00008748706,0.000007238893,0.0008832762,0.9577845,0.00008120752,0.003269016,0.003436868,0.002467381,0.0242404],"study_design_scores_gemma":[0.000009622467,0.0001180996,0.004735102,0.0001645829,0.0000127204,0.0005997507,0.962543,0.0003231864,0.001086022,0.001418859,0.02895577,0.00003335029],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9592838,0.0005552609,0.004439206,0.008469096,0.0003153491,0.00006091603,0.00002005409,0.0001177577,0.02673855],"genre_scores_gemma":[0.9911996,0.0002566385,0.001359575,0.001032414,0.00004919969,0.0000453247,0.00001355633,0.00005663978,0.005987194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01119073,"threshold_uncertainty_score":0.0339914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02355346149486312,"score_gpt":0.3298685073280495,"score_spread":0.3063150458331863,"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."}}