Is it Actually Violence? Framing Technology-Facilitated Abuse as Violence
Bibliographic record
Abstract
When discussing the term “Technology-Facilitated violence” (TFV) it is often asked: “Is it actually violence?” While international human rights standards, such as the United Nations’ Convention on the Elimination of All Forms of Discrimination against Women, have long recognized emotional and psychological abuse as forms of violence, including many forms of technology-facilitated abuse, law makers and the general public continue to grapple with the question of whether certain harmful technology-facilitated behaviors are actually forms of violence. This chapter explores this question in two parts. First, it reviews three theoretical concepts of violence and examines how these concepts apply to technology-facilitated behaviors. In doing so, this chapter aims to demonstrate how some harmful technology-facilitated behaviors !t under the greater conceptual umbrella of violence. Second, it examines two recent cases, one from the British Columbia Court of Appeal (BCCA) in Canada and a Romanian case from the European Court of Human Rights (ECtHR), that received attention for their legal determinations on whether to define harmful technology-facilitated behaviors as forms of violence or not. This chapter concludes with observations on why we should conceptualize certain Technology-Facilitated behaviors as forms of violence.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".