Addressing Anti‐Black Racism in English Language Teaching: Experiences from Duoethnography Research
Bibliographic record
Abstract
Abstract Anti‐Black racism can be difficult to discuss in English language teaching because teachers often feel unprepared. This article describes our experiences as researchers and educators from a duoethnographic self‐study to understanding the possibilities of addressing social justice issues in an adult English as a second language (ESL) classroom. Using the concepts of anti‐racism and solidarity, we explored how teachers can plan, deliver, and evaluate lessons that resonate with the students' academic needs, while also addressing discrimination against marginalized communities. We gathered data from conversations via Zoom and electronic communications as well as various classroom materials and analyzed them to find emerging themes. The data revealed that addressing anti‐Black racism in the ESL classroom comes with tensions about sparking trauma among students, a lack of time to prepare the content, and how to create safe spaces for students. This article proposes that despite the difficulties teachers might experience when addressing these topics, vigorous work must be done to actively challenge the privileges and oppression that are present not only in classroom practices but also in personal experiences.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".