Rethinking Gendered Violence Through Critical Feminist Community-Engaged Research
Bibliographic record
Abstract
This article analyzes how the conceptualization of gendered violence shapes responses and possibilities for redress in two very different community-engaged research contexts and projects. The first case study examines how Canadian universities enact sexual violence policies from the perspective of student activists and other stakeholders to understand the struggle over the power to define violence and shape institutional responses. The second case study is a participatory action research project that explores how transnational feminist and human rights regimes shape, inform, and often occlude or over-determine the struggles for redress by Indigenous women survivors of wartime sexual violence in Guatemala. In both contexts, we identify the persistent circulation of a particular ‘violence against women’ paradigm that functions as a universalizing exceptionalist imaginary which excludes more complex and situated understandings of violence while legitimizing certain responses over others. We consider the possibilities of critical community-engaged research as a means of challenging this presumed universalism. We explore the complexities of conducting such research as white scholars located within the neoliberal academy, given how its investment in community engagement serves to mask the implications of academic knowledge production in colonial and imperial projects and positions the university and the researcher as “saviours” of the “community.”
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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 | 0.068 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.159 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".