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Record W2980545579 · doi:10.1177/0031512519881598

Academic Timing and the Relative Age Effect Among Male and Female Athletes in Canadian Interuniversity Volleyball

2019· article· en· W2980545579 on OpenAlexaffabout
Sabrina Safranyos, Laura Chittle, Sean Horton, Jess C. Dixon

Bibliographic record

VenuePerceptual and Motor Skills · 2019
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAthletesPsychologyQuartileBasketballTrack and field athleticsDemographyCohortPhysical therapyMedicineConfidence interval

Abstract

fetched live from OpenAlex

This study examined the moderating effects of academic timing on the relative age effect in men's and women's U Sports (formerly Canadian Interuniversity Sports) volleyball. Interuniversity sport exists within an academic setting and encompasses different age groups within a single team, making it necessary to account for the academic timing of student athletes when studying the relative age effect. To be considered “on-time,” a student athlete's birthdate and expected athletic eligibility status must coincide, while a “delayed” student athlete will have an athletic eligibility corresponding with a younger cohort. We collected birthdates and eligibility years from the U Sports eligibility certificates of 2,780 male and 3,715 female athletes for the years 2006–2007 through 2013–2014; we then classified athletes as either on-time or delayed. We used a chi-square (χ 2 ) goodness-of-fit tests to compare the observed distributions of student athletes' actual versus “expected” births across each quartile. Our analyses demonstrated an advantage for athletes born in the first half of the selection year. These results suggest that delaying entry into university may help equalize the playing field for relatively younger athletes wishing to compete in U Sports volleyball.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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.

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

Citations8
Published2019
Admission routes2
Has abstractyes

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