‘But she’s not even trans!’: A rhetorical analysis of ‘liberal feminist’ defences of Imane Khelif amid Olympic transvestigations
Bibliographic record
Abstract
In the explosion of discourse across the political spectrum amidst Imane Khelif’s Paris Olympics performance, little academic attention has been paid to the subtle anti-trans and anti-Arab rhetoric posed by supporters of Khelif in defence of her success. In essence, this article adopts the case study of the discursive landscape surrounding Khelif’s Paris Olympics win to demonstrate the theoretical and liberatory limits of exclusionary, so-called ‘liberal’ feminism. As anti-trans sentiments, violence, and legislation are steadily rising across North America, most blatantly within the US, there is an imperative to not only address the overt right-wing transphobia responsible for these attacks, but also the normalization of anti-trans rhetoric among the left. Under the guise of a feminist orientation, these ‘liberal feminists’ defend Khelif’s eligibility to compete by touting her cisgender womanhood – silently reinforcing the notion that transgender and intersex athletes are deserving of that violence and exclusion. These arguments also suggest the impossibility of an LGBTQ + identity for Algerian-born Khelif, given her ‘homophobic’ and ‘transphobic’ society: a common Orientalist trope assigned to Arab and Muslim countries to justify Western imperialism. Finally, we analyse the failure of these arguments to address systemic forms of gender-based violence in sport, including sex testing, which disproportionately affects racialized women athletes from the Global South.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".