Breadth and Depth of Vocabulary Knowledge and Their Effects on L2 Vocabulary Profiles
Bibliographic record
Abstract
Breadth and depth of vocabulary knowledge have been studied from many different perspectives, but the related literature lacks serious studies dealing with their effects on vocabulary profiles of EFL learners. In this paper, with an aim to fill this gap, the relative effects of breadth and depth of vocabulary knowledge on L2 vocabulary profiles were analysed. In the first stage of the study, learner essays (n=84) and native essays (n=75) were compared in terms of vocabulary profiles through the use of an online database, and each participant obtained vocabulary profile scores from four different levels. In the next stage, the learners’ depth and breadth of vocabulary knowledge were measured by using two different vocabulary tests, and then the scores they obtained on these tests were hierarchically regressed on their profile scores. The results suggested that both breadth and depth of vocabulary knowledge had significant and robust effects on the L2 vocabulary profiles of the EFL learners; however, depth of vocabulary knowledge appeared to be a better predictor of vocabulary profiles than breadth of vocabulary knowledge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".