The development of second language productive vocabulary \nin an intensive ESL classroom
Bibliographic record
Abstract
The aim of this study was to investigate how second language (L2) productive vocabulary develops over time by targeting multiple dimensions of word knowledge. The research questions addressed were: a) How does the productive vocabulary of 11-12 year-old L2 learners in an intensive ESL program in Quebec develop over time with regard to vocabulary size, lexical richness, and lexical depth? and b) What similarities or differences can be observed between written vocabulary development and spoken vocabulary development? The study drew on a 58,000-word written corpus and a 28,000-word spoken corpus produced by 108 beginner-level francophone learners of English (11-12-year-olds). Data analyses were based on several measures which included counts of word families, percentages of 1K, 2K, and 3K+ words, types-per-family ratios, and counts of 2-word lexical bundles, all analyzed using the tools available at www.lextutor.ca. Two-way analyses of variance (ANOVAs) with modality (written, oral) and time (T1, T2) as between-participants factor were used to compare the four learner corpora (written and oral at T1 and T2). Findings indicate that learners’ written vocabulary developed in all three dimensions (vocabulary size, lexical richness, and lexical depth), and that their spoken vocabulary improved only in lexical richness but showed no significant growth in size and lexical depth. The results suggest that overall learners performed better in written than in spoken modality, which could be attributed to a) task-specific effects, b) differences between spoken and written language access, and c) differences in the nature of spoken and written vocabulary used.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".