“You Did It to Yourself”: An Exploratory Study of Myths About Gender-Based Technology-Facilitated Violence and Abuse Among Men
Bibliographic record
Abstract
Gender-based technology-facilitated violence and abuse (GBTFVA) is a common experience for those engaging with digital technologies in their everyday lives. To better understand why GBTFVA persists, it is necessary to understand the false beliefs and cultural narratives that enable and sustain them. Drawing on the literature on rape myths, this paper explores the prevalence of seven gender-based online violence myths among Canadian men. To achieve this, we adapted the Illinois Rape Myth Acceptance (IRMA) (Payne et al., in J Research in Personality 33:27–68, 1999) to assess GBTFVA, and surveyed 1,297 Canadian men between 18 and 30 years old on their GBTFVA beliefs. Our results show that GBTFVA myths and cultural narratives are prevalent across participants, though endorsement levels vary. Four myths were more strongly endorsed: It Wasn’t Really Gender-Based Online Abuse , He Didn’t Mean To , Gender-Based Online Abuse Is a Deviant Event , and She Lied. Overall, these findings help to name and thus begin to address the narratives that sustain and perpetuate gender-based online 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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".