Transformative plurilingualism pedagogies in English academic writing: instructor perceptions
Bibliographic record
Abstract
This study investigates the possibility of plurilingual pedagogies in English for Academic Purposes (EAP) and English Academic Writing (EAW) to promote inclusion. Drawing on the notion of plurilingualism (Coste et al. Citation2009), this study focuses on instructors’ perception and treatment of students’ home languages in the EAP/EAW classroom. Qualitative semi- structured interviews were conducted with 10 EAP/EAW instructors from two Canadian universities in uniquely bilingual contexts. Reflexive thematic analysis (Braun and Clarke Citation2021a, Citation2021b, Citation2025) was used to analyze the data for semantic and latent meaning. The data shows the dominance of prevailing, monolingual ideologies and the underutilization of students’ full linguistic repertoire despite recognition of the social, cultural, and linguistic value of being plurilingual. Data further suggests tensions between educational best practices, the place of students’ home languages, and promoting inclusion through teacher-led plurilingual pedagogies. These tensions arise from a monoglossic conceptualization of language as fixed and stable, in contrast to the messiness and fluidity of plurilingualism, which is heteroglossic. Only by embracing a heteroglossic orientation to language can instructors feel capable of integrating students’ linguistic diversity into the classroom.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".