Bibliographic record
Abstract
Abstract Once seen as a neglected area, second language vocabulary research has come into its own in recent years. But classroom implementations have been slow to follow. One potentially very useful research finding is the impressive coverage power of a relatively small number of words: analyses of large corpora of language show that with knowledge of the 2,000 most frequent word families of a language, learners will be familiar with around 80% of the words they encounter. This position paper argues for refocusing language pedagogy to improve learners’ opportunities to acquire knowledge of these important words. The rationale is based on empirical studies showing how knowledge of vocabulary generally and 2,000 high frequency families in particular impact proficiency. Research also shows that “normal” classroom input does not support the acquisition of the words learners most need to know. Résumé Autrefois vue comme négligée, la recherche sur les connaissances en vocabulaire en langue seconde s’est imposée depuis quelques années. La mise en oeuvre dans les classes n’a suivi que lentement. Un résultat des recherches avec du potentiel est la couverture impressionnante que donne un nombre de mots relativement restreint. Des analyses de grands corpus démontrent qu’avec la connaissance des 2 000 familles de mots les plus fréquentes d’une langue, les apprenants seront familiers avec environ 80% des mots qu’ils rencontreront. Cet exposé de position plaide en faveur de recentrer la pédagogie des langues afin d’améliorer les possibilités pour les apprenants d’acquérir la connaissance de ces mots importants. La justification est fondée sur des études empiriques qui démontrent que les connaissances en vocabulaire en général et des 2 000 familles les plus fréquentes en particulier donnent l’avantage dans la maîtrise d’une langue. Les recherches démontrent aussi que l’apport des activités ordinaires dans les classes n’est pas suffisant pour acquérir les mots dont les apprenants ont besoin.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".