SHOULD FEMINISTS STOP TALKING ABOUT CULTURE IN THE CONTEXT OF VIOLENCE AGAINST MUSLIM WOMEN? THE CASE OF “HONOUR KILLING”
Bibliographic record
Abstract
The violence, scale, and power of anti-Muslim narratives circulated on the internet and elsewhere continue to have considerable impact on feminist antiviolence initiatives. I examine contemporary responses to “honour killings” with particular reference to the Palestinian, Indian, and North American contexts, reflecting on how anti-violence advocates negotiate the terrain of culture in the case of honour killings. I ask whether the focus on culture has an impact on how courts and society view violence committed by Muslim men (and sometimes women) against Muslim women and girls. I suggest that cultural details contribute little to an enhanced legal understanding of the crime simply because this is not their primary purpose. Instead, the cultural details are part of a pedagogy that conveys a message of the racial and cultural superiority of the dominant society and a corresponding inferiority of Muslim cultures. We should therefore always talk culture with the greatest of restraint lest the racism that accompanies culture talk inhibit our understanding of the violence and limit our capacity to respond to it.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| 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".