Being Muslim in a time of fear: intersectional realities of visibility, vulnerability, and resilience in Canada
Bibliographic record
Abstract
Hate crimes, like Islamophobia, have created unprecedented concern in Canada, particularly for visibly Muslim individuals. This SSHRC-funded study examines the lives of South Asian Muslim youth in the Greater Toronto Area (GTA) through 49 semi-structured interviews. Using Crenshaw's intersectional model, this article explores the ways in which race, religion, gender, and immigration status, shape Muslims, especially visibly marked women, as targets of fear, control, and government surveillance. The study underscores how physical markers like the hijab intensify Islamophobic experiences, creating gendered forms of exclusion and hyper-surveillance. While factors like age, class, or immigration status did not significantly alter these experiences, institutional and cultural locations played critical roles. Participants expressed mistrust in conventional support systems and highlighted the lack of culturally responsive mental health services. Building on Zine, J. [2006. “Unveiled Sentiments: Gendered Islamophobia and Experiences of Veiling among Muslim Girls in a Canadian Islamic School.” Equity & Excellence in Education 39 (3): 239–252] and Perry, B. [2015. “‘All of a Sudden, There Are Muslims': Visibilities and Islamophobic Violence in Canada.” International Journal for Crime, Justice & Social Democracy 4 (3)], the findings offer a more nuanced understanding of intersectional Islamophobia and its effects on Muslim youth in Canada.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.042 | 0.013 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 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".