Addressing Racialized Multicultural Discourses in an EAP Textbook: Working Toward a Critical Pedagogies Approach
Bibliographic record
Abstract
Racialized multicultural discourses emerge in the TESOL classroom via textbook representations of immigrant success stories and perceived racial and cultural differences among students. Although liberal multicultural discourses may be well intentioned, these discourses warrant closer examination for the ways in which they can essentialize cultural identities and enact power dynamics of who is defining and who is being defined. Drawing on an ethnographic English for academic purposes (EAP) classroom case study, and with the aim of bridging the gap between critical theories and actual classroom practices, this article explores the approaches an instructor implemented with students in addressing such discourses in an EAP textbook chapter. The article first presents the chapter's passages and then describes how the discourses were initially taken up by the participants. A conversation between the instructor and the author in which the instructor shared a racializing experience is then featured, providing the context for her subsequent approach in which she revisited the reading with students. The author examines the classroom interactions with two questions in mind: How did the instructor's critical pedagogies approach work to mediate the racialized representations in the chapter? Did this approach facilitate more meaningful dialogical engagements enabling the students to develop their academic literacies?
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".