Exploring Islamophobia and Anti-Muslim Racism in Canada and the USA: A Comprehensive Scoping Review
Bibliographic record
Abstract
Hate crimes, driven by bias against race, religion or identity, have increasingly targeted Muslims in Canada and the United States. However, despite growing literature, research remains fragmented, often focusing on specific contexts such as racial bias, media influence or political rhetoric. This scoping review synthesizes the existing literature on Islamophobia and anti-Muslim racism in Canada and the United States, focusing on its prevalence, characteristics and contributing factors, aiming to inform policies and strategies to combat their impacts. Using Arksey and O'Malley’s methodological framework and PRISMA-ScR guidelines, two online databases were searched for articles published in English from 1995 to 2023. The search identified 31 articles to be included in the research. The analysis highlighted four major themes in the literature: politically-driven anti-Muslim hate, media-driven anti-Muslim hate, gendered hate crimes against Muslims, and online hate crimes against Muslims. The findings illustrate how certain politically charged rhetoric and policies (e.g., Canada’s Bill 21) normalize hostility toward Muslims and intensify public prejudice. Media portrayals that frame Muslims as violent reinforce negative stereotypes, further fueling discrimination. Gender-based violence disproportionately affects visibly Muslim women, as attire like the hijab makes them identifiable targets in public spaces. Online hate crimes, facilitated by the anonymity of digital platforms, continue to grow in both frequency and impact. These findings indicate an urgent need for more inclusive public policy initiatives, targeted educational efforts, and ongoing research to address the various forms of anti-Muslim hate. Further research including diverse perspectives and non-English literature will contribute to a deeper understanding of the problem.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.042 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".