Providing controlled exposure to target vocabulary through the screening and arranging of texts
Bibliographic record
Abstract
This article considers the problem of how to bring foreign language students with a limited vocabulary knowledge, consisting mainly of high-frequency words, to the point where they are able to adequately comprehend authentic texts in a target domain or genre.It proposes bridging the vocabulary gap by first determining whic h word families account for 95% of the target domain's running words, and then having students learn these word families by reading texts in an order that allows for the incremental introduction of target vocabulary.This is made possible by a recently developed computer program that sorts through a collection of texts and a) finds texts with a suitably high proportion of target words, b) ensures that over the course of these texts, most or all target words are encountered five or more times, and c) creates an order for reading these texts, such that each new text contains a reasonably small number of new target words and a maximum number of familiar words.A computer-based study, involving the sorting of 293 Voice of America news texts, resulted in the finding that a) the introduction of new target vocabulary in each text could be kept to a reasonably small amount for the majority of texts, and b) the number of target vocabulary items occurring fewer than five times could be kept to a minimum when the list of target vocabulary accounted for 96% of the domain's running words, rather than 95%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".