Scientific Neglect: Cis-Bias in the Sociology of Sport’s Approach to Trans Athletes
Bibliographic record
Abstract
In light of the growing interest in trans athletes, this article explores how research on this population has been conducted within the sociology of sport from 2006 to 2021. Drawing on concepts from trans studies and Viviane Namaste’s notions of erasure and oversight, this paper highlights the presence of cis-biases—understood as an interpretation of transness rooted in a cis-centered perspective. These cis-biases are identified at three levels: (1) Theoretical: Limited integration of trans studies, leading to the erasure of trans knowledge; (2) Methodological: Barriers to accessing the field and a lack of participant diversity; and (3) Analytical: A focus on athletes’ bodies and the individualization of discrimination. Ultimately, this article calls for a stronger dialogue between trans studies and the sociology of sport.
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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 | 0.102 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.013 | 0.118 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".