{"id":"W4409259395","doi":"10.5539/elt.v18n5p1","title":"Navigating Vocabulary Learning in Mobile-Assisted Language Learning: Mapping Benefits and Addressing Challenges","year":2025,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology; Vocabulary learning; Vocabulary; Language acquisition; Vocabulary development; Mobile device; Linguistics; Mathematics education; Teaching method; Computer science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002188063,0.0002971969,0.0003514125,0.0003021809,0.0004988423,0.0004444864,0.0006110077,0.0001811422,0.000009221725],"category_scores_gemma":[0.002755953,0.0003310975,0.00006378434,0.0005087709,0.00004627873,0.0007509216,0.000523795,0.003011013,0.000004965185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000169414,"about_ca_system_score_gemma":0.00007164491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002071622,"about_ca_topic_score_gemma":0.00005274595,"domain_scores_codex":[0.9969327,0.0009711852,0.0004452997,0.0007968164,0.0003039956,0.0005499976],"domain_scores_gemma":[0.9983438,0.0007968128,0.00023447,0.0004806981,0.00004870048,0.00009553076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.00000192537,0.00004409883,0.01054862,0.0001067188,0.00001359053,0.00003671101,0.3172426,0.004335366,0.001326326,0.0008569655,0.000005079964,0.665482],"study_design_scores_gemma":[0.002310625,0.0002389166,0.08136976,0.009873957,0.0000425254,0.00008673088,0.7868809,0.108789,0.001879701,0.0001243914,0.006674608,0.001728882],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9573219,0.02912716,0.005553955,0.0002938939,0.000501975,0.0002867805,3.732748e-7,0.0009687063,0.005945211],"genre_scores_gemma":[0.9845282,0.0001823971,0.01448942,0.0001145312,0.0002329118,0.0000881204,0.00001739282,0.00003541083,0.0003116344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6637531,"threshold_uncertainty_score":0.9999141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01784393409418936,"score_gpt":0.295426907911102,"score_spread":0.2775829738169127,"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."}}