Body classification in sport: A collaborative autoethnography of two female athletes
Bibliographic record
Abstract
Two female athletes’ embodied experiences in two different aquatic nature based sports are explored using collaborative autoethnography in conjunction with Foucault’s theory of the body as a site of discipline. The first section of this paper provides an overview of literature addressing body practices occurring in sport as a means of better contextualising how sporting sites have come to privilege female athletes’ bodies that are ‘fatless’, ‘fit’, ‘idealised’ and ‘feminine’ over those who did not meet such body standards. In the second part of the paper, collaborative autoethnography is used as a means of presenting and analysing two female athletes’ embodied experiences in aquatic nature based sports. The two female athletes’ stories reveal how their bodies were ‘classified’ according to the idealised female athletic body shape for their specific sport. The two female athletes’ stories also revealed that as a result of their bodies being classified in the sporting context, a fractured body-self relationship resulted.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".