English for Academic Purposes in Canada: Results From an Exploratory National Survey
Bibliographic record
Abstract
The growing trend of internationalization at Canadian institutions of higher education has led to increased need to support plurilingual students using English as an additional language (EAL). This support, often embedded in English for academic purposes (EAP) programs, is offered in a wide range of contexts across Canadian institutions of higher education. However, relatively little is known about this sector or those who work within it. In this article, we seek to delineate the Canadian EAP landscape by providing findings from the first phase of a mixed methods investigation into EAP programs and practitioners across Canada. We surveyed EAP programs and practitioners across three types of Canadian institutions involved in the provision of EAP support (n = 481). Findings point to a diversity of program models and practitioner profiles across Canadian regions and institutions, as well as significant differences in practitioners’ professional satisfaction based on role and institution type. Further findings point to substantial concern among EAP practitioners regarding job security, collaboration with other institutional stakeholders, and professional development opportunities. Adopting a critical pragmatic lens, we discuss findings, raising questions for consideration for EAP administrators, instructors, and post-secondary institutional policy makers, and conclude with a call for greater research into Canadian EAP programs and practitioners.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".