Culture Talk and the Politics of the New Right: Navigating Gendered Racism in Attempts to Address Violence against Women in Immigrant Communities
Bibliographic record
Abstract
Right-wing policy approaches addressing violence against women often draw from xenophobic conceptions of racialized groups as culturally backward. While antiracist, anticolonial feminist scholarship has convincingly critiqued this, how to talk about culture in a context of gendered racism remains pressingly unresolved. In our work, we examine how the politics of culture shape policies and practices designed to combat violence against women in the Canadian immigration context. Using interviews with fifteen service providers conducted in 2011–12 during a time of heightened attention to violence against women among South Asian immigrants, we show how advocates challenged stigmatizing conceptions of violence as cultural, rejecting what we call “culture talk” in favor of more structural explanations. However, advocates also struggled to account for what we would call the “cultural specificities” of the violence they witnessed, often substituting the term “community” for “culture” to avoid racialization. We then analyze parliamentary debates surrounding the 2015 right-wing Conservative government passage of the Zero Tolerance for Barbaric Cultural Practices Act, which targeted forced marriage, polygamy, and honor-based violence. Right-wing politicians incorporated both “culture” and “community” to deflect accusations of racism while nevertheless engaging in racializing discourse, illustrating the limits of the turn to “community.” Acknowledging the dangers of culture talk, our analysis builds on feminist scholarship to call for renewed approaches to talking about culture—not as a totalizing force but as situated practices of meaning making that inform all acts of violence and responses to violence.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.044 | 0.057 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".