{"id":"W2734743001","doi":"10.5539/elt.v10n8p135","title":"Using the Vocabulary Self-Collection Strategy Plus to Develop University EFL Students’ Vocabulary Learning","year":2017,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Education and Critical Thinking Development","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Psychology; Vocabulary learning; Mathematics education; Significant difference; Data collection; Equivalence (formal languages); Treatment and control groups; Vocabulary development; Control (management); Test (biology); Teaching method; Statistics; Linguistics; Computer science; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002761215,0.0001247872,0.0001335524,0.00008440593,0.00676245,0.0007141479,0.0008290094,0.00009490741,0.0001219161],"category_scores_gemma":[0.003074002,0.000116084,0.00004118402,0.0002139335,0.00009376813,0.0003901568,0.0002483675,0.0006513497,0.00002121138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006589468,"about_ca_system_score_gemma":0.0005790635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003736133,"about_ca_topic_score_gemma":0.0009796459,"domain_scores_codex":[0.9979571,0.0007479389,0.0001426938,0.0002642618,0.0005286902,0.0003592817],"domain_scores_gemma":[0.9991564,0.0001663287,0.00009620499,0.0002788108,0.0001384058,0.0001638085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001086717,0.00006430767,0.01961667,0.000009155332,0.00004840576,0.000042642,0.9657388,0.0002076993,0.00007148746,0.007947996,0.0003841472,0.005857779],"study_design_scores_gemma":[0.0004382788,0.0000314412,0.01652535,0.0001045885,0.00005363294,0.000002916103,0.9154794,0.0002440213,0.00007743511,0.0001597092,0.06648225,0.000400975],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9536093,0.00004951681,0.002574829,0.0008635874,0.0008968785,0.000270117,6.655805e-7,0.0002937901,0.04144129],"genre_scores_gemma":[0.9860552,0.00001176413,0.006180178,0.0002887317,0.0005772643,0.000002723783,0.000003965124,0.0000153321,0.006864863],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0660981,"threshold_uncertainty_score":0.9945306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03264528190304125,"score_gpt":0.3519754810085473,"score_spread":0.319330199105506,"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."}}