Bibliographic record
Abstract
Abstract The last decade has seen significant positive changes in global attitudes, policies and practices that impact the lives of trans people. Meanwhile, the world of sport has been notoriously slow to follow these social justice initiatives. In fact, sport has the dubious distinction of lagging behind almost every other western social organization on issues of discrimination, whether based on sex, gender, ‘race’, ethnicity, social class, religion or ability. Underlying these trends is the binary thinking that has formed the basis for gender categories of sport and physical activity for over a century. The introduction begins as Helen Lenskyj extends the issue of justice for trans athletes beyond the scope of sport. Next, the contemporary socio-political contexts in the US, UK, and beyond are outlined. A brief description of the common ground between justice for trans and intersex athletes is provided, while noting that the focus of this book is on trans athletes. An overview of terminology is presented. Ali Greey then describes their personal experience competing for Canada as a non-binary athlete. Engaging Gleaves and Lehrbach's (2016) work, their argument challenges the viability of making trans-exclusive physiological equivalency synonymous with a rhetoric of fairness. Finally, the authors explain the volume's analytic frameworks and present an overview of the contents, summarizing the key themes and findings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".