Conclusion: Challenges, Struggles and the Way Forward
Bibliographic record
Abstract
Abstract Formidable social-cultural and legal challenges face trans athletes, particularly trans girls and women, at the global, national and local levels. Two underlying and mutually reinforcing themes are in evidence throughout these analyses: the principle of sport exceptionalism, and the power of the media to shape trans-related discourse. The longstanding concept of ‘sport exceptionalism’ is routinely invoked to justify trans girls' and women's exclusion: that is, rules applying to other social contexts and workplaces must be suspended in relation to sport, so that women's ‘safety’ and ‘fairness’ may be guaranteed. Mainstream and social media contribute to trans exclusionary attitudes, by spreading misinformation and promoting a moral panic over the spectre of trans women taking over girls' and women's sport. Detailed analyses of media treatment of trans athletes Laurel Hubbard and Lia Thomas demonstrate these trends. Moreover, media play a significant role when they are reporting on global, national and local developments in sport policies and practices, with media distortion of scientific findings exacerbating these problems. An examination of conceptual and applied responses to these challenges provides the context for exploring the way forward: new ways of imagining sport that are inclusive and just.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".