Using Canadian Law to Prevent, Respond to and Remedy Maltreatment in Sport: Listening to and Learning from Athletes
Bibliographic record
Abstract
This thesis addresses maltreatment of athletes in Canada, in the post-Nassar era, by considering applicable law, policy, academic literature and a qualitative study. Athlete maltreatment may include: psychological, physical and sexual maltreatment, and neglect. Prevalence and impacts of maltreatment are examined. Legal and administrative options available to complainants are discussed, as well as applicable international human rights and child rights conventions, Canadian legislation, legal principles, and jurisprudence. An academic literature review provides maltreatment definitions in order to lay the groundwork for the discussion. Academic perspectives and proposals for redress are considered. A qualitative athlete study produced four key themes which may negatively impact athletes: lack of education on maltreatment, distorted priorities, self-regulation by sport organizations and a discriminatory sport culture. Fortunately, many athletes and academics are united in a mission to promote a new holistic vision that prioritizes the health and wellbeing of athletes rather than athletic victories.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.041 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".