Language Ideologies and English for Academic Purposes Writing: A Case in Ontario
Bibliographic record
Abstract
The field of language instruction is crucial in Canada, given the number of newcomers seeking to improve their English (or French) language skills after arrival. For those who plan to enter post-secondary education but do not meet required language proficiency scores, English for Academic Purposes (EAP) programs provide opportunities to strengthen linguistic and academic skills. These inarguably pragmatic goals are often unquestioned, yet EAP instruction is an ideological undertaking with social, economic, and political consequences. This qualitative study investigates language ideologies – rationalizations and justifications for language use and form – through interviews with EAP writing instructors. As participants discussed the material they taught, the language skills students developed, and the consequences of studying EAP writing, ideologies regarding what forms of language should be taught, the purposes of academic writing (instruction), and the social and political dimensions of language were (re)produced and resisted. Formative influences on these ideologies included education, upbringing, and personal language learning experiences. Developing a critical awareness of the understandings of language that inform teaching and learning can make more transparent the linguistic and social discourses that circulate within and beyond EAP writing classrooms and help instructors, students, and other EAP community members (re)produce or resist them strategically.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.041 | 0.014 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".