Bibliographic record
Abstract
OBJECTIVE: To determine the perceptions and roles of referees about violence and injury in hockey games. DESIGN: Questionnaire. SETTING: Web-based survey. PARTICIPANTS: We contacted referees across Canada from various leagues and all levels of play, with the majority of respondents from Ontario (92%). MAIN OUTCOME MEASURES: We gathered demographic information anonymously and posed questions on aggression and experience in hockey games. RESULTS: The majority of referees (n = 632) indicated that violence is a serious concern to both players and referees at all levels of hockey. More than 90% of referees responded that they were the recipients of aggression and anger (92.1%, 95% confidence interval, 90.0-94.2), 55% had been involved in hockey games where aggressive behavior resulted in the referee losing control of the game, and 71% said that this increased aggression leads to injury. Referees' opinions are that the coach is the most responsible for managing on-ice safety (63%). To improve hockey safety, referees suggest education and more rigorous enforcement of discipline for all participants. CONCLUSIONS: Referees are important for hockey safety and need to be appropriately supported. Referees believe that increased aggression can lead to injury and that rules need to be enforced more diligently. Referees recommend that increased education about safety is needed to guide parents, coaches, and players to make hockey safer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".