“She’s Twice Their Age”: Representations of Aging and the Creation of an Age Order in Women’s Gymnastics
Bibliographic record
Abstract
Elite-level women’s artistic gymnastics is (in)famous for the youth of its competitors. Yet if age representation constructs the limits and possibilities of sport, it is important to explore the effects of these representations. Here we examine media coverage of gymnast Oksana Chusovitina, who has competed internationally up to the age of 46. We conduct content analysis of 17 international competitions in which Chusovitina competed, from 2001 to 2018, exploring coverage of her and the younger gymnasts with whom she competed. As hegemonic masculinity structures a gender order in sport and beyond, we argue that gymnastics coverage constructs an age order in the sport by (1) emphasizing the exceptionality of older competitors; (2) focusing on athletes’ private lives; and (3) constructing the athlete-coach relationship as familial, where coaches are represented as surrogate parents. These techniques diminish the agency of young gymnasts and produce their youthfulness as hegemonic, treating athletes competing beyond their twenties as unworthy of serious attention. When older athletes like Chusovitina are categorized as outliers, the sports media is free to infantilize younger gymnasts, naturalizing their ostensible lack of agency, as well as the sport’s high burn-out and injury rate.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".