Navigating Inclusion Beyond the Binary: A Content Analysis of Transgender and Non-Binary Athlete Participation Policies
Bibliographic record
Abstract
In recent years, the inclusion of transgender and non-binary athletes in sport, specifically trans women, has become a politically charged and highly contested issue, often sparking debate based on ideology that overshadows scientific literature. In 2022, the NCAA changed its longstanding transgender athlete participation policy, deciding to let each sport’s national governing body (NGB) set the eligibility guidelines and requirements for transgender and non-binary athletes (NCAA, 2022). Recent literature (Canadian Centre for Ethics in Sport, 2022a; Hamilton et al., 2024) challenges the common assumption that transgender women competing in the female category hold a physical advantage over their cisgender peers. In line with this evolving perspective, the International Olympic Committee (2021) has emphasized that no athlete should be excluded from sport based on a “perceived unfair competitive advantage due to their sex variations, physical appearance, and/or transgender status” (p. 4). Building on these developments, the purpose of this study is to conduct a content analysis of current sport participation policies to quantitatively assess the eligibility requirements imposed on transgender and non-binary athletes. This approach aims to address the disconnect between academic literature and the criteria that govern trans inclusion in sport and reveals that many policies continue to impose restrictive eligibility requirements. This study calls for the development of evidence-based, inclusive policies that align with current scientific understanding to bridge the gap between research and regulation.
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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".