{"id":"W4226525969","doi":"10.1177/07356331221083215","title":"Identifying Key Contextual Factors of Digital Reading Literacy Through a Machine Learning Approach","year":2022,"lang":"en","type":"article","venue":"Journal of Educational Computing Research","topic":"Technology Adoption and User Behaviour","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Advanced Education","funders":"","keywords":"Reading (process); Computer science; Context (archaeology); Literacy; Perspective (graphical); Class (philosophy); Mathematics education; Digital literacy; Key (lock); Psychology; Pedagogy; Artificial intelligence; World Wide Web","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":[0.001205699,0.0004885028,0.0005203283,0.003173797,0.0005710198,0.001487638,0.0003495482,0.0003738341,0.0027962],"category_scores_gemma":[0.005842926,0.0001880399,0.0008917272,0.002109787,0.0005469144,0.001005167,0.0009421813,0.0006478757,0.0003292177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003955942,"about_ca_system_score_gemma":0.0008011902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005139538,"about_ca_topic_score_gemma":0.009445365,"domain_scores_codex":[0.999001,0.0004663904,0.00009870381,0.0002197602,0.000107603,0.0001065008],"domain_scores_gemma":[0.9960496,0.002854237,0.0005371392,0.0002041914,0.0002136598,0.0001411589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008510329,0.000243662,0.9386646,0.0001075054,0.0001673918,0.0001359309,0.001115095,0.001832418,0.0008502388,0.0006745499,0.0002090687,0.05591452],"study_design_scores_gemma":[0.00001060981,0.0002646927,0.9555346,0.0001199645,0.0001574587,0.0001684713,0.003897781,0.03497002,0.000997038,0.002672628,0.001176145,0.00003069688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819613,0.0002836157,0.01462969,0.0001955397,0.00001088068,0.00008260823,0.0004264172,0.00008236909,0.002327538],"genre_scores_gemma":[0.9929097,0.00008170575,0.006530566,0.00001289135,0.000005699633,0.00004424553,0.0002276224,0.00000399824,0.0001834346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005139538,"threshold_uncertainty_score":0.01021922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3318015714863491,"score_gpt":0.5165587901100406,"score_spread":0.1847572186236915,"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."}}