TECHNOLOGY-FACILITATED VIOLENCE AGAINST WOMEN & GIRLS: ASSESSING THE CANADIAN CRIMINAL LAW RESPONSE
Bibliographic record
Abstract
Alongside increasing awareness of the ways in which digital technologies can be used to facilitate violence against women and girls, there have come questions about the applicability and efficacy of Canadian criminal law responses. Rooted in a feminist perspective and based on a review of over 400 reported cases involving technology-facilitated violence (TFV), the authors argue that Canadian criminal law both can and should respond. Technology-facilitated violence against women and girls (TFVAWG), like violence against women and girls (VAWG) more generally, undermines their rights to sexual integrity, dignity, autonomy and to equal participation in public and private life. Criminal law responses are an important mechanism for expressing public disapprobation of TFVAWG’s negative effects on these fundamental rights. However, the authors’ review reveals certain shortcomings in achieving survivor-centred outcomes. Recognizing these and other limitations of criminal law, the authors also assert that proactive approaches aimed at broader social transformation will be essential to ensuring the full and equal participation of women and girls in a digitally connected world.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".