{"id":"W2402805519","doi":"10.5539/elt.v9n6p242","title":"Investigating the Role of Multiple Intelligences in Determining Vocabulary Learning Strategies for L2 Learners","year":2016,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology; Intrapersonal communication; Metacognition; Theory of multiple intelligences; Vocabulary; Interpersonal communication; Language learning strategies; Cognition; Test (biology); Emotional intelligence; Mathematics education; Developmental psychology; Social psychology; 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":[0.0007930592,0.0004956141,0.0002371163,0.001062455,0.0002813929,0.001248375,0.0002545022,0.0003467404,0.001319814],"category_scores_gemma":[0.003294891,0.0001681591,0.0004037755,0.000489073,0.0003598848,0.0006699016,0.0005584829,0.0004770299,0.0001975025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002972817,"about_ca_system_score_gemma":0.0007876194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003985488,"about_ca_topic_score_gemma":0.004821796,"domain_scores_codex":[0.9996675,0.00007146924,0.00003652311,0.00005057475,0.0001142424,0.00005969468],"domain_scores_gemma":[0.9980189,0.0006608118,0.0006918043,0.00007515644,0.0002685287,0.0002848383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006780847,0.0003001707,0.9703664,0.00005510782,0.00005257838,0.0002763335,0.00361185,0.0001242371,0.001807364,0.0001587312,0.00007018428,0.02310919],"study_design_scores_gemma":[0.000006354542,0.0004001484,0.9919978,0.0000288114,0.00004495055,0.0002766614,0.004565707,0.0007552047,0.001234357,0.0002872639,0.000389395,0.00001327317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989051,0.0001011105,0.0001186379,0.00002427239,0.000001573522,0.000006904166,0.0000138512,0.00000175557,0.0008267829],"genre_scores_gemma":[0.9992664,0.00008755635,0.000243874,0.00001101182,0.000002370903,0.000008938313,0.0000285383,0.000001006417,0.0003502752],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003985488,"threshold_uncertainty_score":0.007924616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817392321717469,"score_gpt":0.2478990585964294,"score_spread":0.2297251353792548,"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."}}