Applications of word lists in second language learning and teaching
Bibliographic record
Abstract
Abstract Although word lists have generated a great deal of attention from researchers, there has been no comprehensive review of the applications of word lists in second language learning and teaching. This article reviews the development, validation, and applications of 50 word list studies that were published and discussed in major international peer-reviewed Applied Linguistics and TESOL journals from 2013 to 2023. It shows that the methodology of word list development and validation has become more sophisticated and word list developers can see many potential applications of their lists in research and pedagogy. However, most applications of recently developed word lists have been restricted to the BNC/COCA lists developed by Paul Nation, and little is known about the degree to which most word lists have been used in pedagogical contexts. Our review indicates several directions for future research on word lists, including exploring the impact of published lists on pedagogy, replicating word list studies for learners in underrepresented contexts, and developing sustainable, low-cost methods of developing word lists to allow teachers and learners to create lists serving their own needs.
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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 | 0.029 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".