Eating Disorders in Canadian Sport History: An Introductory Case Study on Charlene Wong
Bibliographic record
Abstract
There has been a lack of appreciation of the prevalence of eating disorders in Canadian sport history within the literature. Thus, in continuation of my previous research on sport-related disordered eating, a case study was conducted based on the career of Canadian Olympian Charlene Wong. The purpose of this case study was to identify the social causes of eating disorders in action.\nThrough a thorough analysis of interviews and newspaper articles, the early life of Charlene Wong was broken down to understand the development of her maladjusted eating patterns. The results demonstrated that the circumstances in which Wong was encouraged to perform under from as young as 10, initiated her disordered eating. Forces such as thin ideals, perfectionism, and beauty comparison placed Wong in a position to take extreme measures to look and perform her best.\nApplying a historical lens to a true battle with an eating disorder improves the understanding of the reality of disordered eating in Canadian sport, as well as increases the conversation on the contexts in which this condition is most prevalent.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".