Combatting Islamophobia: A Reflection of an Anti-Racism Educator
Bibliographic record
Abstract
Although Islamophobia has been rising in the West, educational institutions continue to struggle with incorporating anti-Islamophobia education into their curricula. Anti-Islamophobia education, which shares similarities with anti-racism education, can help challenge Islamophobia both within and beyond educational settings. Research on Islamophobia in the West, such as studies by Bakali (2016), Halabi (2021), Hossain (2017) and House (2012), predominantly focuses on students’ experiences and voices, often overlooking teachers’ perspectives on their efforts to combat Islamophobia in schools (Niyozov & Pluim, 2009). In this article, I use autoethnography as a methodology to reflect on my efforts to challenge Islamophobia at a secondary school in Quebec. I discuss a project I conducted with my students in my English as a Second Language (ESL) class. This project comprises two key dimensions: a conceptual aspect aimed at challenging racism and a technical component focused on teaching ESL. In this reflection, I concentrate on the issues regarding my practices as an anti-racist pedagogue rather than on my role as an ESL teacher. Although a few students were not comfortable discussing Islamophobia, most demonstrated a solid and critical understanding of it. They were brave enough to lead class discussions and offer different perspectives to challenge Islamophobia in their everyday life.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.027 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".