{"id":"W2071112440","doi":"10.5430/wjel.v5n1p32","title":"Chunking, Elaborating, and Mapping Strategies in Teaching Reading Comprehension Using Content Area Materials","year":2015,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Educational Methods and Media Use","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chunking (psychology); Reading comprehension; Reading (process); Mathematics education; Indonesian; Comprehension; Computer science; Context (archaeology); Perception; Test (biology); Psychology; Artificial intelligence; Linguistics","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":[0.001659383,0.0006667681,0.000553845,0.0006275023,0.0003840593,0.0007194884,0.0006017631,0.0005282029,0.002635509],"category_scores_gemma":[0.006356913,0.0003118199,0.0003121143,0.0003679451,0.0004727129,0.0009529791,0.0006942145,0.0009620027,0.0003255603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002133669,"about_ca_system_score_gemma":0.000676544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004411679,"about_ca_topic_score_gemma":0.0007785462,"domain_scores_codex":[0.998861,0.0005557401,0.00008898891,0.0001996237,0.0002059659,0.00008872096],"domain_scores_gemma":[0.9953324,0.003564304,0.0004326143,0.0002609911,0.0001285054,0.0002811028],"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":[0.005508095,0.06353929,0.0113245,0.002019262,0.0001589812,0.0005014812,0.01606425,0.00149194,0.1679074,0.001863482,0.0006016376,0.7290197],"study_design_scores_gemma":[0.009567345,0.3008835,0.2619873,0.001341277,0.001979734,0.001846261,0.01566346,0.01863764,0.3518169,0.009590394,0.02635504,0.0003310346],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921162,0.0002809314,0.004459888,0.00007171257,0.00002876558,0.0005594339,0.00001921781,0.00004454846,0.002419283],"genre_scores_gemma":[0.9532859,0.0009105329,0.04165588,0.0001045299,0.00005059695,0.001139933,0.00007507607,0.00002310472,0.002754383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002635509,"threshold_uncertainty_score":0.008816659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.080655633687315,"score_gpt":0.3307222448734989,"score_spread":0.2500666111861839,"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."}}