The Role of Critical Discourse Analysis in Promoting Epistemological Diversity in EAL Learning: A Case Study on Canadian International Students
Bibliographic record
Abstract
ABSTRACT English as an additional language (EAL) learning and teaching are epistemic practices with marked epistemic injustice. Language is not merely a tool for communication, but rather a fundamental aspect of how knowledge is constructed, validated, and transmitted across different cultural and intellectual traditions. This study explores the intricate relationship between language as an epistemic apparatus and EAL learning in the Canadian context, with a particular focus on how English language ideologies shape knowledge construction and transmission. Through a qualitative case study approach, international students were guided to use critical discourse analysis (CDA) to examine traditional assumptions embedded in EAL textbooks. The study starts with how the ideology of Standard English reflects Western epistemological traditions as a specific manifestation of epistemic injustice in EAL teaching and learning. This recognition allows international students who speak English as an additional language to critically engage with not just the linguistic forms of English but also the underlying epistemic dominance represented by these forms. By realizing the tacitly agreed‐upon dominance of Western academic English, which represents one form of Standard English, students question and challenge the epistemological hegemony that often accompanies linguistic ideological hegemony. Our findings suggest that introducing CDA as an epistemic tool can raise critical language awareness and increase the epistemological diversity of additional language learning and teaching. This pedagogy reinforces multilingualism in EAL both mentally through multilingual thinking and practically through EAL learning, thereby fostering a deeper appreciation of the diversified epistemological landscape that different languages and cultures bring to academic discourse. It enables learners in multilingual contexts to navigate and negotiate between different knowledge systems, promoting a richer, more nuanced understanding of knowledge creation, dissemination, and absorption.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.040 | 0.018 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".