"It's Like All of Campus Life Inside a Little Classroom": How an English for Academic Purposes (EAP) Program Operates within a University Setting
Bibliographic record
Abstract
English for Academic Purposes (EAP) are language programs designed to assist non-native speakers with their academic studies in English. These programs determine entry and exit into various stages of post-secondary education, depending on English language competence. EAP programs developed into a teaching and learning profession connected to the spread of English as a dominant global language. Although EAP did not originate in universities, Canadian universities adopted these programs to attract international students. Over time, EAP has become an integral part of university education in Canada. Given the clear differences in learning objectives, it is uncertain whether or not universities have the ability to incorporate EAP as a profession. The goal of this research is to discover how an EAP program fits within a degree-granting Canadian university institution. A qualitative methodological case study was conducted in the “English for Academic Success” (EFAS) program at Renison University College affiliated with the University of Waterloo. The history of how EAP became a unique teaching occupation is included to help identify the problems associated with the professional status of EAP within the university system. The sociological literature on “professions” helps deepen an understanding of the challenges EAP educators face in being recognized as professionals, especially within a university environment. With an empirical understanding of the status of EAP in the context of university education, this research contributes to educational theories of professions, work, globalization and the knowledge economy.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.026 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".