‘An San’s hair is short, therefore she is a feminist:’ Women athletes’ hair, feminist movements, and nationalism
Bibliographic record
Abstract
At the 2020 Tokyo Summer Olympics, South Korean archer An San received national attention for her short hair, when anti-feminists accused An of being a feminist. The anti-feminists’ online assaults made national headlines and evoked a strong feminist countermovement. In this paper, we ground this incident in the context of Korean nationalism and its relation to sport, and South Korean feminist movements. Using social media and news sources as our data, we analyze the meanings of women athletes’ hair length and styles, revealed in the case of An San. We explore meanings of hair in Korean women’s elite sport, and within the context of the tal-corset feminist movement. We argue that the An incident sheds light on the complex interplay between nationalism, feminist movements, and physical cultures of hair, and see the An incident as one that drew increasing public attention and support for the feminist movement in South Korea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".