Transpanic and the threats to safe sport for all in Donald Trump’s America 2.0
Bibliographic record
Abstract
During the 2024 US Presidential election, Donald Trump and many supporters capitalised on the transpanic issue to use transwomen in sports as a wedge issue. In Trump 2.0 a misogynistic attitude by boys and men towards women suggested that the recent gains made by girls and women in sport should be contained or reframed in terms of ‘protection’ of women’s sports from a manufactured ‘male’ takeover. LGBTQI+ and transgender athletes have also come under attack under the guise of men protecting girls and women from other imagined predatory ‘men’. In this paper, we examine the social and psychological damage a resurgent white-male dominated masochism is causing for girls, particularly trans and LGBTQI+ females in the USA. We provide recommendations for how inclusion could be promoted that reduces mental health risks and which could reframe the discussions from overtly being overtly political to scientific as sports policy is formulated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".