“Stop Being so Fkn Soft”: Masculinity, Politics, and the Acceptance of Gender-Based Online Violence Myths Among Young Canadian Men
Bibliographic record
Abstract
Defining manhood is a critical concern in contemporary politics, especially due to its increasing role in shaping cultural narratives toward gender-based violence—and in particular, toward gender-based technology-facilitated violence and abuse (GBTFVA). In this context, this study investigates how political affiliation influences perceptions of GBTFVA among young Canadian men. To explore this, we draw on a survey of 1297 young Canadian men who align themselves with ideological affiliations across the political spectrum. Overall, our results show that political ideologies matter when understanding who enacts and sustains GBTFVA, as they significantly shape attitudes toward gender-based violence in digital spaces. Moreover, while we note that conservative participants displayed higher acceptance of GBTFVA myths than their liberal counterparts (such as She wanted it and She asked for it ), findings show that these harmful narratives are endorsed in different yet meaningful ways throughout all ideological affiliations. Furthermore, we found that one myth— It wasn’t really gender-based online abuse —is similarly endorsed across all political affiliations, thus highlighting the scope of these narratives that diminish the experience of targets across political discourses. By illuminating these intersections, this study provides valuable insights into the cultural and ideological underpinnings of GBTFVA, offering leverage points for societal change and prevention efforts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".