{"id":"W4388983646","doi":"10.23977/aetp.2023.071609","title":"Current Situation and Improvement of Secondary Vocational Students' Digital Literacy in China","year":2023,"lang":"en","type":"article","venue":"Advances in Educational Technology and Psychology","topic":"Digital literacy in education","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocational education; Mathematics education; Analytic hierarchy process; China; Information literacy; Digital literacy; Literacy; Questionnaire; Psychology; Process (computing); Medical education; Computer science; Pedagogy; Engineering; Political science; Sociology; Medicine; Social science; Operations research","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":[0.001017877,0.000138168,0.0002115858,0.001366001,0.0007961483,0.001288983,0.0003766549,0.0004097688,0.001820804],"category_scores_gemma":[0.002148557,0.00008026982,0.0001524992,0.001367629,0.0006953004,0.001103242,0.001206923,0.0003643157,0.0001309064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416883,"about_ca_system_score_gemma":0.00356037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01188969,"about_ca_topic_score_gemma":0.01211343,"domain_scores_codex":[0.99926,0.0001255111,0.00009386747,0.00008050118,0.000191585,0.0002486084],"domain_scores_gemma":[0.9984301,0.0001595824,0.0003754931,0.00004308041,0.0003015601,0.0006901812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008620627,0.0002255624,0.8469272,0.000334741,0.00002539998,0.000839141,0.01142691,0.0003974455,0.002613232,0.004358097,0.001125468,0.1316406],"study_design_scores_gemma":[0.0000110243,0.0001893675,0.9786071,0.0001142709,0.00001831656,0.0003516169,0.01159089,0.0007720674,0.0007497452,0.0008198527,0.00675222,0.00002356781],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943823,0.00054574,0.0001282469,0.001171861,0.000007187729,0.000008806095,0.00004248053,0.000008707387,0.003704739],"genre_scores_gemma":[0.9989636,0.0003588322,0.00008238026,0.00006707177,0.000004987077,0.000004209978,0.00003285813,7.331917e-7,0.000485246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01188969,"threshold_uncertainty_score":0.02364099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007075521013708402,"score_gpt":0.3659447600605534,"score_spread":0.358869239046845,"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."}}