MétaCan
Menu
Back to cohort
Record W986187905 · doi:10.1520/stp15246s

A Profile of Rule Infractions in Bantam Level Ice Hockey

2000· book-chapter· en· W986187905 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicSports Analytics and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsIce hockeyPsychologyMedicinePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

The purpose of the present study was to provide a profile of rule infractions at the Bantam (14–15 years old) level of ice hockey. For each penalty, information is provided on the: (a) category and type, (b) period of the game, (c) number of players penalized, and (d) zone and area on the ice surface where the infraction occurred. Gamesheet reports and videotapes of 55 games from five leagues revealed 850 penalties. There was an average of 15.5 penalties per game with most penalties classified as minor aggression penalties (62.4%). The number of penalties tended to increase from the first period to the third period. Most of the penalties were assessed to one player at a time (74.9%); however, the percentage of multiple player penalties tended to increase from one period to the next. More penalties were assessed to players in their defensive zone, and particularly in the area in front of the net.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.926
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0400.002

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 head0.057
GPT teacher head0.220
Teacher spread0.163 · 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

Quick stats

Citations12
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicSports Analytics and PerformanceFrench-language works237,207