Am I Being Framed to Fit? English as Additional Language Learners’ Critical Perspectives on Western Academic Cultural Representation in Textbooks
Bibliographic record
Abstract
English for Additional Language (EAL) textbooks have traditionally served as a primary resource for language learners to receive language input. These textbooks help learners become acquainted with the linguistic aspects of the language, as well as the cultural elements inherent in the English language. However, it is important to note that the language used in EAL textbooks to represent cultures is not neutral and is socially constructed within complex power relations. This study critically examines how two EAL textbooks, used in Canadian higher education, use language to represent Western academic cultures. It also explores the extent to which learners' academic community interactions have been addressed. By conducting Critical Discourse Analysis (CDA) with EAL learners, this study explores the qualitative manner in which features of Western academic cultures are revered and legitimized in two EAL textbooks used in Canadian higher education. The findings show that the EAL textbooks, rooted in different ideologies such as standard language ideology and linguistic imperialism, promote the values, characteristics, and practices of the dominant Western academic culture as a skill set, while excluding other academic cultures and positioning learners as deficient. This study contributes to promoting critical language awareness in English language education and provides insights for teachers and students to question the ideology, norms, and values that are present in curriculum artifacts, such as language textbooks, in order to build a more inclusive and equal learning environment. Additionally, textbook designers and publishers could use the findings to inform future textbook iterations.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".