Bibliographic record
Abstract
Abstract While transgender people have had some success in gaining recognition and human rights in the collection of nations known colloquially as ‘the West’, a well-financed reactionary movement is attempting to roll back these gains. A constellation of white supremacist, conservative, and heteropatriarchal organizations and movements are in collusion with so-called ‘gender critical feminists’ to resist feminist and gender-inclusive challenges to traditional gender and sexual hierarchies by targeting trans girls and women – more so than trans boys, trans men and non-binary people – for surveillance and exclusion (Sharrow, 2021a, 2021b). In the past several years, bills designed to delegitimize and exclude trans people in various ways have been introduced in many US state legislatures. Within this larger anti-trans campaign, bills designed specifically to block trans girls and women from participating in ‘female’ sport have been signed into law in 11 US states to date and proposed in many others. It is no accident that organized sport is a site for contesting the inclusion of transgender people and that transgender girls and women are the primary targets of these campaigns. Debates about criteria for female eligibility and a succession of pseudo-scientific forms of ‘sex testing’ in elite levels of sport highlight both the ideological nature of the two-sex system and the intense material and cultural investment in maintaining its façade. In this chapter, I mobilize moral panic theory to focus specifically on anti-trans campaigns in the United States aimed at preventing trans girls and women from participating in ‘female’ sport as evidence of a testosterone panic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".