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Record W3047989113 · doi:10.1520/stp162520190051

Fair Play in Minnesota Hockey

2020· book-chapter· en· W3047989113 on OpenAlexaboutno aff
Mae R. Moris, Michael J. Stuart, David A. Krause, Kyle Farrell, Michelle H. Caputi, Aynsley M. Smith

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAeronauticsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.364
Threshold uncertainty score0.723

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.038
GPT teacher head0.191
Teacher spread0.153 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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