“HONOUR”- BASED VIOLENCE AND THE POLITICS OF CULTURE IN CANADA: ADVANCING A CULTURAL ANALYSIS OF MULTI-SCALAR VIOLENCE
Bibliographic record
Abstract
Since 2015, in Canada, political discourse on “honour”-based violence has shifted away from highly problematic understandings of “culture” as the cause of violence among racialized, Muslim, and immigrant communities. Instead, talk of culture has dropped out of the equation altogether in favour of more structural definitions of gender-based violence (GBV). In this article, we ask what gets lost when culture is not taken into account when talking about or trying to understand forms of GBV. Drawing from theoretical conceptualizations of culture — defined as “situated practices of meaning-making” that shape all experiences of violence, and societal responses to violence — we argue for a multiscalar approach to culture. To illustrate this framework, we first offer a critical analysis of Aruna Papp’s 2012 memoir Unworthy Creature as an exemplar of stigmatizing uses of culture and a key text promoted by the Conservative federal government at the time. We then turn to interviews we conducted with service providers serving South Asian survivors of GBV in Toronto from 2011 to 2013. Our analysis illustrates how to talk about culture as a key ingredient shaping multiscalar violence, regardless of whether that violence occurs in majority or minority communities. We conclude with three policy implications for addressing HBV moving forward.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.043 | 0.034 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".