Transgender Women Athletes and Elite Sport: A Scientific Review
Bibliographic record
Abstract
The inclusion of transgender people in sport is relatively new and has proven to be complex. The impacts of inclusion policies, or lack thereof, are significant and reach far beyond sport to affect how trans people are included in other areas of society. Sport can have a positive impact on the physical and mental health of transgender people and can contribute to lifesaving opportunities and enhanced wellness.In 2021, the CCES commissioned E-Alliance to complete a review of scientific and grey literature on transgender athlete participation in competitive sport. Transgender Women Athletes and Elite Sport is a review of research articles published in the English language between 2011 and 2021 inclusive. The report is divided into two sections, one that encompasses biomedical studies and a second that encompasses sociocultural studies.The report’s authors recommend that all reasonable efforts should be made to make sport inclusive and accessible for transgender individuals. However, the scope of this review was limited to binary trans women who are elite athletes and was not sport specific. As a result, the conclusions are not directly applicable to other trans or non-binary populations and other levels of sport.This literature review is one component of a larger scope of work that aims to support leaders and policy makers and provide access to critical research insights.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".