Widening the Circle: Teaching English for Specific Purposes in the Light of Content-Based Instruction
Bibliographic record
Abstract
Language instruction based on content is not a new idea; it originated in English-speaking countries such as the USA, Canada, and many European countries that study content subjects in English. Accordingly, it has become a widely adopted pedagogical approach to English for Academic Purposes. The teaching of English to speakers of other languages, including Algeria, however, abounds with multiple acronyms, causing teachers to become confused, and even disoriented when considering English for Specific Purposes, English for Academic Purposes, and Content-Based Instruction. The rationale of this purely theoretical-based article is to understand the current pedagogical practices in language across the curriculum and strive to unearth and uncover how English for Specific Purpose courses can be taught by implementing Content-based Instruction as a syllabus, by reviewing some linguistic, and pedagogical rationales as well as principles for the application of this framework for foreign language learners in higher education, more precisely in the ESP context. Besides, the study suggests some teaching models that are meant to help English language instructors to be content teachers in some circumstances and language-competent teachers in other contexts.
 Keywords: Content-Based Instruction; teacher; Teaching English to Speakers of Other Languages.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".