Promoting athlete welfare: A proposal for an international surveillance system
Bibliographic record
Abstract
Efforts to ensure the welfare of athletes have long existed in sport but have heightened recently across numerous countries in response to shocking revelations of sexual abuse in sport. Cases such as the sexual abuse of female gymnasts by a team doctor in the U.S. and sexual abuse of male footballers by a coach in the U.K. have drawn significant attention and scrutiny by stakeholders in sport and the public alike. These and other cases indicate that in spite of existing athlete welfare policies, educational programmes, and efforts to ensure compliance, numerous athletes were abused, the perpetrators were permitted to continue over an extended period of time, and some adults knew of the abuses and were complicit in failing to intervene. In this article, the authors use Bronfenbrenner’s Ecological Theory to review the current landscape with respect to initiatives to prevent and address athlete maltreatment at each level of the theory. The authors also propose that to advance athlete welfare, more attention needs to be devoted to the development of interventions at the macrosystem or international level. Using Bruno Latour’s concept of the oligopticon (1992) an argument is forwarded to create an international surveillance system to promote athlete welfare.
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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 | 0.063 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".