Getting Jacked and Burning Fat: Examining Doping and Gender Stereotypes in Canadian University Sport
Bibliographic record
Abstract
In this paper, the authors analyze the results of a three-year qualitative study examining Canadian female and male university student athletes’ perceptions of gender and doping. Student athletes (n = 38) discussed their perceptions of doping, gender, and sport during in-depth, semistructured interviews. The results demonstrate the extent that student athletes continue to draw on gender stereotypes in assessing acceptable and unacceptable substance use in sport. Many of the student athletes interviewed acknowledged or applied extensive gender stereotyping in discussing their understanding of femininity and masculinity in sport. Women athletes, in particular, indicated they were hesitant to use both banned and permitted ergogenic supplements out of fear of becoming too muscular or masculine, while several male athletes expressed feeling pressure to appear muscular, especially if they wanted to be successful in traditionally male-dominated sports such as football. Applying previous theoretical work Lock by (2003) and Bartky (1990) to the themes resulting from the interviews, the authors argue that student athletes’ attitudes toward doping remain engulfed in gender stereotypes. Because doping culture in the Canadian university system is entangled with gender stereotyping, doping education and prevention programs would benefit from taking these gender stereotypes into account, rather than pretending they do not exist.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".