{"id":"W4386553581","doi":"10.19173/irrodl.v24i3.7220","title":"Shifting Conversations on Online Distance Education in South Korean Society During the COVID-19 Pandemic: A Topic Modeling Analysis of News Articles","year":2023,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ode; Latent Dirichlet allocation; Sociology; Pandemic; Distance education; Social distance; Coronavirus disease 2019 (COVID-19); Topic model; Media studies; Social science; Computer science; Mathematics; Pedagogy; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003149093,0.0003970783,0.0003228585,0.004159986,0.001313826,0.00334363,0.000341512,0.0006109765,0.001180118],"category_scores_gemma":[0.008066386,0.0002254647,0.0005369383,0.004911105,0.001074956,0.004081903,0.001717115,0.0007646275,0.000392432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120271,"about_ca_system_score_gemma":0.001111504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005561361,"about_ca_topic_score_gemma":0.009056783,"domain_scores_codex":[0.9984085,0.0007655796,0.0001567755,0.0002469612,0.0002506886,0.0001715844],"domain_scores_gemma":[0.9852733,0.01092952,0.001954637,0.0003996968,0.001071116,0.0003717127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006239178,0.0003358743,0.4193254,0.002137256,0.0002586555,0.002513135,0.4037479,0.002651672,0.01040361,0.007861969,0.006833746,0.1433069],"study_design_scores_gemma":[0.00002170327,0.0001847802,0.4381033,0.0007676645,0.0002748352,0.001023778,0.4751973,0.0174914,0.003704733,0.004337573,0.05873875,0.0001541098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905086,0.001070061,0.003020386,0.001048232,0.00005846926,0.00006415349,0.001072689,0.00002880488,0.003128682],"genre_scores_gemma":[0.9921143,0.001156018,0.003211948,0.0001945708,0.00009423278,0.000093124,0.001444598,0.00002940267,0.001661771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005561361,"threshold_uncertainty_score":0.01665419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2617930914288815,"score_gpt":0.5432302129928894,"score_spread":0.281437121564008,"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."}}