A Language Teacher in the ESP Classroom: Can We be a Successful Dweller in This Strange and Uncharted Land?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The English for specific purposes (ESP) classroom has been described by a number of scholars as a strange and uncharted land for many language teachers. This is because ESP is designed to meet the specific needs of the learner, making it related to subject specialist content. Accordingly, some people feel that language teachers without a specialist subject background are “unqualified” to teach ESP courses. Rather, subject specialists should be the ones who teach these courses even though, in many cases, they are not trained to teach language. This paper therefore aims to find out whether it is possible that language teachers, who have limited subject specialist knowledge, can “settle down” happily in this strange and uncharted land. Reviews of the literature and previous studies of related topics, namely the definition of ESP, subject specificity, subject specialist knowledge, strategies for dealing with a lack of subject specialist knowledge, and the roles of the ESP practitioner, are discussed first. Then, the answer to the question is presented at the end.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it