{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001158893,0.00009542688,0.0001434635,0.0009794631,0.00247962,0.0002368342,0.0006288516,0.0001162456,0.0002306],"category_scores_gemma":[0.0009043405,0.00007330647,0.00002671464,0.002820315,0.001022082,0.00005290323,0.0002099751,0.0004083656,0.00005159051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004009631,"about_ca_system_score_gemma":0.002802076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005315557,"about_ca_topic_score_gemma":0.0003232984,"domain_scores_codex":[0.9983031,0.0002521906,0.0001891883,0.0003382773,0.0005282727,0.0003889526],"domain_scores_gemma":[0.9980551,0.001270771,0.00004960349,0.0002200902,0.0003006165,0.0001038246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004235739,0.0008558315,0.220608,0.000007448163,0.0005389003,3.993561e-7,0.02312687,0.000009648786,0.000005415167,0.6284988,0.1149766,0.01132972],"study_design_scores_gemma":[0.0003421397,0.0001024864,0.1898594,0.0001017423,0.00007151071,0.000001258567,0.1882082,0.00002699863,0.0004284161,0.1084942,0.5120862,0.0002775535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5350333,0.0003521033,0.00007056967,0.3547602,0.001020238,0.000730273,0.000003427524,0.00003486828,0.1079949],"genre_scores_gemma":[0.9432613,0.0000619652,0.0007776856,0.0004953343,0.0002972115,0.0006408567,0.0000314238,0.000004719374,0.05442949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5200046,"threshold_uncertainty_score":0.998819,"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."}}