Child Abuse in the Canadian and American Olympic Movement: Reforming Institutional Oversight of Amateur Sports
Bibliographic record
Abstract
For many years, sexual abuse of young athletes quietly festered throughout amateur sport organizations in Canada and the United States. However, the veil has recently been lifted by the highly publicized testimonies of athletes sexually abused as minors by disgraced former USA Gymnastics (“USAG”) physician and Michigan State University professor Larry Nassar. The troubling details of these stories spurred investigations seeking to identify the causes behind the institutional failures to intervene in Nassar’s perpetration of abuse. The evidence gathered by these investigations alongside independent academic research reveal that unfettered predatory behaviour is a pervasive issue across the institutions responsible for overseeing the American Olympic movement and amateur sports. In addition, similar patterns of unchecked abuse have since been identified as plaguing Canadian Olympic and amateur sport organizations. This paper examines the institutional failures to address child sexual abuse occurring under the oversight of Olympic and amateur sport organizations in Canada and the United States. In both countries, efforts are currently underway to reform governance of these institutions to better protect minors participating in amateur sports. Accordingly, this paper also analyzes the policies implemented thus far and makes substantive recommendations on ideal federal level initiatives.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".