{"id":"W4285060659","doi":"10.3102/ip.22.1880833","title":"Identifying Factors Affecting PISA 2018 Digital Reading Literacy via Machine Learning Algorithms","year":2022,"lang":"en","type":"article","venue":"","topic":"Education and Learning Interventions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Reading (process); Artificial intelligence; Literacy; Machine learning; Algorithm; Psychology; Pedagogy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004465074,0.0001365829,0.000123572,0.0002529899,0.001142758,0.0009797859,0.0006002343,0.00002030749,0.001156522],"category_scores_gemma":[0.0001485196,0.0001373248,0.0001539021,0.0005741824,0.00001650018,0.001298926,0.0006060775,0.0005557772,0.0001110286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389679,"about_ca_system_score_gemma":0.00002936889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002049195,"about_ca_topic_score_gemma":0.000002545278,"domain_scores_codex":[0.9985671,0.0001804429,0.0002645448,0.0003858833,0.0003141756,0.0002878697],"domain_scores_gemma":[0.9991786,0.0002385524,0.0001583339,0.0002809687,0.00004619826,0.00009728206],"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":[0.000008233275,0.0009453206,0.5486901,0.0001073445,0.0002164767,0.00005960432,0.1073283,0.005441286,0.002287965,0.01259441,0.005983174,0.3163377],"study_design_scores_gemma":[0.001017409,0.0007645959,0.08015427,0.0001568792,0.00003095112,0.0004298018,0.01661617,0.6960881,0.001777675,0.001969277,0.1993712,0.001623664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1825408,0.00008740203,0.8100362,0.0004074342,0.001647696,0.000104863,0.000002793544,0.000634271,0.004538585],"genre_scores_gemma":[0.9675055,0.000001233206,0.01047413,0.0001061821,0.00006858726,0.00001156368,0.00003766905,0.00001684399,0.02177835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.799562,"threshold_uncertainty_score":0.9997566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150131250226805,"score_gpt":0.3173748452287099,"score_spread":0.2858735327264418,"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."}}