Reading academic English: Carrying learners across the lexical threshold
Bibliographic record
Abstract
The ESP reading problem With the growth of English as the lingua franca of work and study, many non-English speakers find themselves needing to attain some level of proficiency in English in order to function in jobs or courses. However, they may have limited time to devote to language learning, and little interest in knowing English outside the work or study context. Responding to these circumstances, English for Specific Purposes (ESP) curriculum designers have attempted to reduce the time frame of learning through domain targeting. They attempt to identify and teach the lexis, syntax, functions and discourse patterns most commonly used in a domain (for chemistry students, test tubes, passive voice, clarification requests and laboratory reports). This approach has given waiters, tour guides and airline pilots enough English to function in their domains after relatively short periods in the classroom. But it runs into complications when the specific purpose is to read extended texts in a professional or academic domain. It now seems clear that the cross-domain generalities of English (pronoun system, verb tenses, basic vocabulary, etc.) can be introduced and practised within a subset of the language. Simple reading tasks such as understanding signs and instructions can be undertaken knowing only the English used in a particular job or profession. But does this hold true for reading longer texts? Consider the position of the learner who knows the grammar of English and the technical terms of a domain: text analysis shows that these terms are typically rather few (Flowerdew, 1993c), roughly 5% of tokens (Nation, 1990).
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".