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Record W2079150696 · doi:10.1080/02640414.2011.593040

Effects of a rule change that eliminates body-checking on the relative age effect in Ontario minor ice hockey

2011· article· en· W2079150696 on OpenAlexaffabout
David J. Hancock, Bradley W. Young, Diane M. Ste‐Marie

Bibliographic record

VenueJournal of Sports Sciences · 2011
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsQuartileAthletesIce hockeyContrast (vision)Selection (genetic algorithm)DemographyStatisticsPsychologyMathematicsComputer scienceMedicinePhysical therapyPhysical medicine and rehabilitationConfidence interval

Abstract

fetched live from OpenAlex

Relative age effects in sport reflect an over-representation of athletes born early in a selection year that lead to selection and performance advantages. These effects might be enhanced by rules that increase physicality. An opportunity to investigate these influences arose when Hockey Canada altered its body-checking rules. Two studies are described that investigate the possible influence of this rule change. Study 1 used cross-sectional data to contrast relative age effects for 9-year-olds in games with and without body-checking (birth quartile 1 ranged from 27 to 39%; birth quartile 4 from 10 to 20%). Study 2 used quasi-longitudinal data to examine age effects when players transitioned from a season in which body-checking was permitted to one that prohibited such checking (birth quartile 1 ranged from 27 to 39%; birth quartile 4 from 11 to 20%). Chi-square statistics demonstrated relative age effects in both studies irrespective of body-checking. Post-hoc analyses indicated reductions in these effects that were limited to some second and third quartiles when body-checking was prohibited. Body-checking is not a critical mechanism of relative age effects. The physicality of ice hockey, regardless of body-checking, and increased experience in ice hockey are influential.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.085
GPT teacher head0.311
Teacher spread0.226 · 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 teacher head, 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

Citations15
Published2011
Admission routes2
Has abstractyes

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