{"id":"W4407760747","doi":"10.5539/hes.v15n2p54","title":"A Bibliometric Analysis of Digital Literacy in Remote Learning","year":2025,"lang":"en","type":"article","venue":"Higher Education Studies","topic":"Technology-Enhanced Education Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics education; Literacy; Higher education; Trend analysis; Technological literacy; Computer science; Educational technology; Psychology; Pedagogy; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006514538,0.0004672828,0.0008981208,0.1245821,0.001582709,0.005394383,0.0007734114,0.0006992946,0.003810502],"category_scores_gemma":[0.04551809,0.0001987826,0.001064533,0.1912241,0.001045104,0.003860291,0.002498708,0.0005286726,0.0008946233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002351498,"about_ca_system_score_gemma":0.004198378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006905486,"about_ca_topic_score_gemma":0.007346251,"domain_scores_codex":[0.9900193,0.002066754,0.001773771,0.0009015125,0.004820465,0.0004181526],"domain_scores_gemma":[0.949075,0.03200012,0.007619775,0.002368131,0.008109012,0.0008279742],"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":[0.0001494891,0.0001893632,0.6643288,0.006195982,0.0007548336,0.0008700885,0.01045542,0.003848905,0.002403957,0.01636389,0.01490654,0.2795327],"study_design_scores_gemma":[0.00002648814,0.0001702294,0.8658096,0.001465681,0.0005926749,0.001528046,0.01625925,0.009796022,0.00240367,0.007970671,0.0938611,0.000116596],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8486927,0.01247652,0.008770697,0.00186617,0.0002346325,0.0006442262,0.04243782,0.0005636292,0.08431355],"genre_scores_gemma":[0.9674517,0.006373192,0.007795377,0.00006195948,0.0002095071,0.0004277158,0.01489008,0.00006746523,0.002722934],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8754179,"threshold_uncertainty_score":0.03445256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04904270243456772,"score_gpt":0.4588463093699769,"score_spread":0.4098036069354092,"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."}}