"Our Experiences are Different...Our Risks are Different": Racialized Women's Online Activism to End Violence Against Women in Canada
Bibliographic record
Abstract
Existing research on online activism to end violence against women tends to homogenize the experiences of women. To help address this issue, this dissertation poses the following questions: why and how do racialized women in Canada participate in anti- violence online activism, specifically around violence against women? Grounded in an intersectional framework, this dissertation draws from semi-structured interviews and uses a social constructivist grounded theory approach to examine the experiences of nine racialized online activist women in Canada. In line with the intersectional framework guiding this research, and in an effort to further both research and discussion as it pertains to the diversity of women involved in such activities, all analyses will also attempt to account for other identities expressed by participants. Broadly, the collective experiences of these participants involves a focus on creating a variety of digital media technologies (e.g., personal websites, social media pages and profiles, and podcasts) to draw attention to the intersectional nature of violence against women. The counternarratives that they create and distribute challenge prevailing narratives that tend to ignore the intersectional nature of violence against women, specifically pointing to omissions in: mainstream news media; the non-profit sector involved in preventing and ending violence against women; and settler colonial policies, frameworks, and regulations. The racialized women interviewed for this dissertation expressed a struggle to increase their visibility and widen their networks for support and mobilization online. As noted by participants, this struggle often arises from a range of social media platform biases and experiences of technology-facilitated violence, factors that are often ignored in research and discussion pertaining to such online activist efforts. By focusing on the intersectional identities expressed by participants and their experiences with struggles unique to online activist work, this study contributes a deeper and more nuanced account to the limited research on racialized women in Canada and their efforts to end violence against women.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".