{"id":"W4388911240","doi":"10.5430/jct.v12n6p318","title":"Enhancing Japanese Reading Comprehension Skills among Students: An Instructional Model Perspective","year":2023,"lang":"en","type":"article","venue":"Journal of Curriculum and Teaching","topic":"Educational Methods and Media Use","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mahasarakham University","keywords":"Reading comprehension; Vocabulary; Computer science; Mathematics education; Grammar; Comprehension; Reading (process); Construct (python library); Psychology; Syntax; Perspective (graphical); Reciprocal teaching; Linguistics; Natural language processing; Artificial intelligence","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":[0.0007868044,0.0004636863,0.0002547229,0.0004950671,0.0003370807,0.0009779489,0.0006421761,0.0005346281,0.002101007],"category_scores_gemma":[0.001630308,0.0001612672,0.0003093887,0.0001974787,0.0004505606,0.0009865838,0.0005101954,0.0006398201,0.0002322756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119896,"about_ca_system_score_gemma":0.001719388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001737761,"about_ca_topic_score_gemma":0.003319787,"domain_scores_codex":[0.9996156,0.0002164127,0.00001367811,0.00004290142,0.00007776463,0.00003357943],"domain_scores_gemma":[0.9993887,0.0002897519,0.00006750738,0.00003099418,0.0001372717,0.00008578463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007762413,0.01169307,0.0734991,0.003188191,0.0002472921,0.001153703,0.04752255,0.02891174,0.09401964,0.1160225,0.008699098,0.6142669],"study_design_scores_gemma":[0.001437207,0.02877152,0.1237427,0.002910864,0.002052622,0.001651257,0.04266342,0.4802492,0.08485927,0.08751203,0.1438548,0.0002951826],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8363037,0.001055165,0.1046986,0.004467497,0.00008517869,0.0009576464,0.0001241736,0.0004083967,0.05189976],"genre_scores_gemma":[0.9297328,0.0007331558,0.06372908,0.0002328453,0.00001607604,0.0007165504,0.00006703756,0.00001550104,0.004756972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002101007,"threshold_uncertainty_score":0.008125484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570694476717796,"score_gpt":0.3466679379194401,"score_spread":0.3309609931522621,"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."}}