“I Bet You Don’t Get What We Get”: An Intersectional Analysis of Technology-Facilitated Violence Experienced by Racialized Women Anti- Violence Online Activists in Canada
Bibliographic record
Abstract
Despite growing attention to violence that women face in online settings, a relatively small proportion of academic work centres on the experiences and perspectives of racialized women in Canada. Informed by an intersectional framework, I draw on semi-structured interviews with nine women across Canada, all of whom are involved in anti-violence online activism, about their experiences of technology-facilitated violence (TFV). Their experiences revealed less prominent narratives, including instances of TFV beyond instances of intimate partner violence (IPV) and beyond sources of anonymous trolling by supposed white men, such as violence perpetrated by peers, white women, and racialized men. In this article, I also include reflections by the interviewees on violence they unexpectedly perpetrated through their online content. These perspectives demonstrate how varied and complex experiences of TFV are beyond instances of IPV and sexual violence. I conclude that when we leave out intersectionality as an approach that centres marginalized groups and broadens our understanding of violence, we are missing out on these more complex experiences of TFV that women face. Thus, I suggest that, to best tackle TFV, policy recommendations and legal remedies need to consider TFV through an intersectional lens.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.041 | 0.016 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".