{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412174,0.0004304796,0.0005346819,0.0006715117,0.0002915632,0.0005320398,0.0005415871,0.0002590537,0.001288693],"category_scores_gemma":[0.002047251,0.0001356625,0.0003808641,0.0002733383,0.0002689504,0.0004428759,0.00071349,0.0004177022,0.0002992912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003247936,"about_ca_system_score_gemma":0.001215331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004497993,"about_ca_topic_score_gemma":0.001305298,"domain_scores_codex":[0.9991791,0.0002717718,0.00008317919,0.0001133205,0.0002648339,0.00008787929],"domain_scores_gemma":[0.9990805,0.000338995,0.0001479649,0.00006899452,0.0001248085,0.0002386724],"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":[0.0006918323,0.03811835,0.04680926,0.001035737,0.00009456518,0.0005472674,0.009481041,0.0007378063,0.04758087,0.000915357,0.001394471,0.8525934],"study_design_scores_gemma":[0.001483415,0.1458199,0.6282408,0.001033573,0.0007172219,0.003566961,0.02540174,0.004761576,0.1335096,0.003222589,0.05199157,0.0002511458],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99568,0.0001434043,0.001824234,0.00006568533,0.00001472247,0.0003721874,0.00002301306,0.00003925802,0.001837536],"genre_scores_gemma":[0.9599131,0.0006213175,0.03400535,0.0001193821,0.00001458581,0.0009597368,0.0001576937,0.00001045888,0.004198441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001412174,"threshold_uncertainty_score":0.007468343,"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."}}