{"id":"W4408362973","doi":"10.1007/s11423-025-10482-1","title":"Correction: Analyzing the discourse on open educational resources on Twitter: a sentiment analysis approach","year":2025,"lang":"en","type":"article","venue":"Educational Technology Research and Development","topic":"Social Media and Politics","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Sentiment analysis; Open educational resources; Computer science; Educational technology; Data science; World Wide Web; Natural language processing; Psychology; Mathematics education","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":[0.006255598,0.002313252,0.002205455,0.005171042,0.00579736,0.006114572,0.004125026,0.01251077,0.06689018],"category_scores_gemma":[0.1775878,0.001236254,0.001443358,0.004912267,0.00454965,0.004575432,0.00523748,0.01823084,0.04026252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005049341,"about_ca_system_score_gemma":0.008356589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02576081,"about_ca_topic_score_gemma":0.0283088,"domain_scores_codex":[0.9907615,0.00183858,0.001634975,0.001108092,0.003765559,0.0008913748],"domain_scores_gemma":[0.8606499,0.03493087,0.006587707,0.00756248,0.08625421,0.004014917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00003207126,0.000004893207,0.00009838706,0.0001751281,0.00001513361,0.0001844283,0.0004348947,0.00002943595,0.00008556392,0.0008363419,0.9954864,0.002617181],"study_design_scores_gemma":[0.00005694183,0.00002192928,0.00161351,0.0007249934,0.00006295876,0.0003594732,0.001287566,0.00050617,0.0007366769,0.001846186,0.9926952,0.00008844736],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.0004521409,0.0004300778,0.0008304007,0.1876592,0.8050156,0.00004507759,0.002556783,0.000517423,0.002493187],"genre_scores_gemma":[0.04954395,0.004844639,0.007278479,0.254777,0.392726,0.001020767,0.004511514,0.003357276,0.2819403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06689018,"threshold_uncertainty_score":0.22377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08425553220883399,"score_gpt":0.464611177221714,"score_spread":0.38035564501288,"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."}}