Gender-Based Violence Against Trans* Individuals: A Netnography of Mary Gregory’s Experience in Powerlifting
Bibliographic record
Abstract
In the context of sport, a growing body of research has reported the prevalence of violence against athletes, including sexual, physical, and psychological violence and neglect, experienced by both women and men in sport. Preliminary research has reported that gender-diverse individuals, specifically transgender athletes, may have a greater vulnerability to experiences of violence in sport, but this remains an under-researched population. In addition to limited research specifically on violence experienced by transgender athletes in sport, there is also only emerging research on virtual violence against athletes, with previous research on virtual violence in sporting spaces highlighting how online spaces are sites that can foster widespread hostility and violence. This study builds on previous research by examining discourses of virtual violence faced by transgender powerlifter, Mary Gregory, following her expulsion from the 100% Raw Powerlifting Federation. This research used a netnographic approach-an online ethnographic case study design. Data were collected from online news sources, as well as social media platforms, including Instagram, Twitter, and YouTube and were analyzed using reflexive thematic analysis. The data provided an insight into the cyberculture of powerlifting, and the negotiation of space, or lack thereof, for Mary Gregory within this physical culture. Five themes of were generated, including invalidation of gender identity, dehumanization, infliction of derogatory and crude language, accusations of cheating, and being compared to cisgender athletes without nuance. The study highlights the presence of significant vitriol across virtual platforms directed at Mary Gregory and the underlying presence of negative gender-based violence again trans* (GBV-T*) discourse. This case provides examples of virtual gender-based violence and transphobia in sport, a lack of readiness to accept trans* athletes, and concerns for the safety of trans* athletes in sporting spaces.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".