<i>It’s not fair!</i> Constructing gendered legal subjects via trans-exclusionary sport legislation
Bibliographic record
Abstract
Ongoing efforts to exclude trans people from the public sphere in the United States include the proposal – and, often, passing – of Bills seeking to exclude trans people from sport. Using Critical Discourse Analysis, we analyse four such Bills. We argue that, in seeking to regulate the participation of trans students in school athletics, legislatures are producing essentialist gendered subjects. Sporting spaces are amenable to such legislation because they are strongholds for simplistic, binary conceptualisations of sex and gender. Further, by operationalising an instrumental view of sport – wherein winning and thus achieving material reward motivates participation – legislatures can construct trans girls as threats to cisgender girls’ future success and mobilise affect and emotion to both produce subjects and to justify transphobic discrimination. This paper contributes to literature on the outcomes of trans-exclusionary regulations by exploring the rhetorical work done by such regulations and what regulation and discipline these strategies make possible.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.048 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".