Something’s Got to Give: Reconsidering the Justification for a Gender Divide in Sport
Bibliographic record
Abstract
The question of whether transgender athletes should be permitted to compete in accordance with their gender identity is an evolving debate. Most competitive sports have male and female categories. One of the primary challenges with this categorization system, however, is that some transgender athletes (and especially transgender women) may be prevented from competing in accordance with their gender identity. The reason for this restriction is because of the idea that transgender women have an unfair advantage over their cisgender counterparts; this is seen as a problem since sports are typically guided a principle called ‘the skill thesis’, which suggests that sports are supposed to determine who is most skillful by maintaining a fair starting point. In this paper, I argue that if the skill thesis ought to be maintained and there continues to exist no conclusive evidence in support of unfair advantages possessed by trans women, then we may want to re-consider the gender binary in sport. Rather than having male/female categories, it may make more sense to categorize athletes based other sport-specific factors (e.g., height, weight, etc.). This may help to maintain the skill thesis while at the same time removing potentially unfair and discriminatory barriers against transgender athletes.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.106 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".