Anti-Muslim Hate Crimes in Canada : Racism, Gendered Violence, and Misogyny
Bibliographic record
Abstract
This article, based on the authors’ work supported by a Social Sciences and Humanities Research Council of Canada – Insight program research grant, briefly outlines the main issues concerning gendered racism and misogyny that arose from 51 semi-structured interviews conducted by the authors. The resulting testimonials evidence recognition of the existence of latent Islamophobia issues being kept alive in Canada. Mercier-Dalphond and Helly point out that institutional factors to systemic racism concerning hate crime violence against Muslims are manifest when the state or judicial processes do not punish violent acts; related problems include victims not receiving proper police attention when seeking justice and a reluctance among security forces and politicians to consider such violent Islamophobia as hate crimes. Since the subject of Canadian white supremacist groups, which are described in this article as posing a real threat in terms of hate crime violence, is beyond the scope of this article, the authors focus mainly on the rich terrain of the gendered, racist nature of anti-Muslim hate crimes 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".