Complexities in Canadian Legal Approaches to Sports Injury
Bibliographic record
Abstract
It is unclear from Canadian case law what the appropriate legal standards of care and regulation should be in athlete injury cases. This chapter provides an overview of existing legal standards and explores the question of participant liability in sport, especially ice hockey. It reviews the applicability of tort law, including both intentional torts and unintentional torts, and considers the applicability and impact of the notion of ‘volenti non fit injuria’ (or voluntary assumption of risk).,The chapter is based on a review of Canadian case law.,Canadian courts have adopted varying standards whereby it is seemingly easier to prove negligence in certain provinces than others. We discuss the implications of these conflicting jurisdictional standards and the need for clearer and more consistent legal guidelines. Further, we show why appropriate legal standards should extend beyond purely objective and legalistic interpretations to more subjective and sociological factors that place sports violence and sports injury in social context.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.027 | 0.037 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".