The Potential of Centralized and Statutorily Empowered Bodies to Advance a Survivor-Centered Approach to Technology-Facilitated Violence Against Women
Bibliographic record
Abstract
Abstract As the means and harms of technology-facilitated violence have become more evident, some governments have taken steps to create or empower centralized bodies with statutory mandates as part of an effort to combat it. This chapter argues that these bodies have the potential to meaningfully further a survivor-centered approach to combatting technology-facilitated violence against women – one that places their experiences, rights, wishes, and needs at its core. It further argues that governments should consider integrating them into a broader holistic response to this conduct. An overview is provided of the operations of New Zealand's Netsafe, the eSafety Commissioner in Australia, Nova Scotia's Cyberscan Unit, and the Canadian Centre for Child Protection in Manitoba. These types of centralized bodies have demonstrated an ability to advance survivor-centered approaches to technology-facilitated violence against women through direct involvement in resolving instances of violence, education, and research. However, these bodies are not a panacea. This chapter outlines critiques of their operations and the challenges they face in maximizing their effectiveness. Notwithstanding these challenges and critiques, governments should consider creating such bodies or empowering existing bodies with a statutory mandate as one aspect of a broader response to combatting technology-facilitated violence against women. Some proposed best practices to maximize their effectiveness are identified.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".