Islamophobic violence as a form of gender-based violence: a qualitative study with Muslim women in Canada
Bibliographic record
Abstract
This paper represents the first empirical qualitative research study on Islamophobic violence against Muslim women in the Canadian context, and presents a novel characterisation of Islamophobic violence against Muslim women as a form of gender-based violence. Twenty-one Muslim women in Toronto and its surrounding areas were interviewed regarding their encounters with Islamophobic violence: they disclosed over 30 incidents, only three of which were reported to police. The spectrum of Islamophobic violence disclosed by participants includes attempted femicide, physical assault, sexual assault and verbal assault. Moreover, two participants disclosed situations of intimate partner violence (IPV) that were entangled with Islamophobic abuse, representing a hitherto uncharacterised intersection of Islamophobia and IPV in the Canadian context. All incidents of physical and sexual violence disclosed by participants were said to have been perpetrated by white men. Many participants believed that they were targeted for Islamophobic violence because of the impact of gendered Islamophobic discourses that construct Muslim women as being passive, weak and oppressed – and therefore as ‘acceptable targets’ for violence. I offer the novel term ‘Islamophobic gender-based violence’ in order accurately name the reality of violence that Muslim women face in the nexus of misogyny and Islamophobia.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".