Not fat, not skinny, functional enough to finish: interrogating constructions of health in the Ironman Triathlon
Bibliographic record
Abstract
Sport has long been popularly promoted as a way to improve one's health through participation. Socio-cultural scholars investigating sport-related pain and injury, however, have called into question this suggested relationship pointing especially to competitive and contact sports as examples that challenge this assumption (Theberge, 2008 Theberge, N. 2008. “Just a normal bad part of what I do”: Elite athletes’ accounts of the relationship between health and sport. Sociology of Sport Journal, 25: 206–222. [Crossref], [Web of Science ®] , [Google Scholar]. “Just a normal bad part of what I do”: Elite athletes’ accounts of the relationship between health and sport. Sociology of Sport Journal, 25, 206–222; Waddington, 2004 Waddington, I. 2004. “Sport, health, and public policy”. In Sporting bodies, damaged selves: Sociological studies in sports-related injury, Edited by: Young, K. London: Elsevier. [Google Scholar]. Sport, health, and public policy. In K. Young (Ed.), Sporting bodies, damaged selves: Sociological studies in sports-related injury. London: Elsevier; Young, 2004 Young, K. 2004. “Sports-related pain and injury: Sociological notes”. In Sporting bodies, damaged selves: Sociological studies of sports-related injury, Edited by: Young, K. 1–25. Oxford: Elsevier. [Crossref] , [Google Scholar]. Sports-related pain and injury: Sociological notes. In K.Young (Ed.), Sporting bodies, damaged selves: Sociological studies of sports-related injury (pp. 1–25). Oxford: Elsevier). In this paper, I use autobiographical and interview materials to explore how people who participate in Ironman triathlons construct health; I reveal the ways that in this non-contact, non-professional sport, ideas that connect health to body image and functionality, emerged most frequently in my analysis. I suggest that these constructions are influenced by neoliberal health discourses, in particular, the notions of individual responsibility, the use of leisure (non-work) time for self-work and (albeit to an extreme end) anti-obesity messages. Insights in this paper serve to further challenge popular assumptions that participation in sport is unquestionably healthy even in non-competitive, individual sport scenarios.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.043 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".