Fair Play in Minnesota Hockey
Bibliographic record
Abstract
Fair Play (FP) is a behavioral modification program originally created in Quebec, Canada, by Edmund Vaz when injuries, violence, aggressive infractions, and expense drastically decreased ice hockey registration numbers. He found that emphasizing sportsmanship had the potential to reduce dangerous plays. FP awards teams one additional point in the district standings after each game if the following criteria are met: (a) the team remained below the predetermined penalty minute threshold, (b) a coach was not assessed a game misconduct, and (c) none of the spectators were ejected from the arena. In collaboration with the Mayo Clinic, Minnesota Hockey adopted FP successfully in 2004. However, it was only applied to district games, accounting for just 36% of the games in a team's season. FP is currently a loosely recommended model in tournaments and plays no role in nondistrict games. In a study of two youth hockey tournaments, the tournament governed by intensified FP found significantly fewer head hits than the tournament without FP. Junior gold-level tournaments also experienced fewer injuries and concussions when using FP. Despite its established effectiveness, FP is underutilized by Minnesota Hockey. The Mayo Clinic Ice Hockey Research Team (MCIHRT) has therefore determined that FP requires a complete relaunch. The Minnesota Hockey board members, district leaders, and MCIHRT have agreed that increasing visibility and establishing universal application by Minnesota Hockey leadership are the most urgent needs. The MCIHRT action plan addressing shortcomings includes: (a) making a FP announcement before and after games; (b) publishing shortened, universal booklets solely regarding FP; (c) coordinating a distribution of new booklets to coaches, officials, and players/parents; and (d) creating webpages on all district and association sites including the rules of FP and access to the FP standings. Finally, creative ways to increase FP's power to favorably modify behavior will be solicited.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".