Sport-related concussion (SCR) prevention and the nature of sport: possibilities and limitations
Bibliographic record
Abstract
Concussions are traumatic brain injuries that can result from a blow to the head or a jolt to the body. Athletes in many sports are exposed to concussion risks. There is a growing concern in sport and society about sport-related concussions (SRC) and an increasing awareness of the importance of proper diagnosis, treatment, and prevention. A traditional, reactive approach emphasizes sound protocols in cases of suspected SRC. A proactive approach involves identifying various causes of SRC and implementing preventive measures. For example, to reduce SRC prevalence in Canadian youth ice hockey, a ban on body checking has been introduced. When a preventive SRC measure implies a change in the main rules of a sport, it impacts its nature and alters how it is played. This can lead to tensions between SRC prevention and sporting concerns. By employing an extended consequentialist framework that includes normative analyses of the values of sport, I will examine the possibilities and limitations of preventive SRC measures in terms of constitutive rule changes. More specifically, I will propose a consequentialist framework to determine the conditions under which athlete exposure to SRC risks in a given sport can be considered unacceptable, acceptable, and even valuable.
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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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".