From <i>Faible</i> to Strong: How Does Their Vocabulary Grow?
Bibliographic record
Abstract
Abstract: The study drew on an 80,000-word corpus consisting of narrative texts produced in response to picture prompts by 210 beginner-level francophone learners of English (11-12-year-olds). The unique feature of the corpus is its longitudinal character: The samples were collected at four 100-hour intervals of intensive language instruction, during which time students made considerable progress in listening and speaking. However, analysis of these staged sub-corpora using Laufer and Nation's 1995 Lexical Frequency Profile did not identify the expected increase in use of less frequent words. Further analyses using three measures available at (a Greco-Latin cognate index, a count of word families, and a types-per-family ratio) showed that although the learners continued to use large proportions of frequent words, their productive vocabulary featured fewer French cognates, a greater variety of frequent words, and more morphologically developed forms. Implications for frequency-based vocabulary acquisition research and vocabulary teaching are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".