Reframing Technology-Facilitated Gender-Based Violence at the Intersections of Law & Society
Bibliographic record
Abstract
This introductory article proceeds in three parts. First, it discusses the origins of this special issue as part of a multi-event, SSHRC-funded conference that focused on pushing beyond a narrow conception of TFGBV; rather than approaching TFGBV as solely an issue of interpersonal behaviours, the animating objective of the conference was to examine the structural, systemic, and design factors that contribute to TFGBV. Second, it explores the importance and promise of reframing TFGBV in this way through intersectional and structural lenses. Third, it briefly highlights some of the key insights from each of the contributions in this special issue. It begins with the theoretically grounded social science insights of Rajani and Gosse focused, respectively, on racialized women’s experiences with TFGBV and on the culture of responsibilization of TFGBV targets. It then shifts to Turnbull’s analysis of corporate responsibility and potential legal liability for ecosystemic factors that contribute to TFGBV. Next, it looks at the legal analyses offered by Stevens and Sali, first on non- consensual disclosure of intimate images (NDII) through the lens of Quebec personality rights, and then on the challenges of addressing NDII through copyright law. Finally, it turns to the contribution of Dunn and Aikenhead, which considers the contested authorship of digital evidence in common law TFGBV cases.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".