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Record W2189659458 · doi:10.1260/1747-9541.10.5.797

Relative Age Effects in Welsh Age Grade Rugby Union

2015· article· en· W2189659458 on OpenAlexaboutno aff
Jason Lewis, Kevin Morgan, Stephen‐Mark Cooper

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2015
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsWelshClubQuarter (Canadian coin)DemographyTypologyPsychologyBiosocial theorySelection (genetic algorithm)DanishOddsSocial psychologyGeographyMedicineSociologyPersonalityStatisticsMathematics

Abstract

fetched live from OpenAlex

Relative age effect (RAE) refers to the immediate and long-term consequences of age difference within an age grouping. In sporting contexts, it has been widely shown that those born in the first quarter gain an advantage over those born in the last quarter of the year. Rugby Union has received scant attention in relation to RAE. The primary purpose of the present study was to examine the presence and prevalence of RAE in Welsh age grade Rugby Union. A further purpose was to consider how coaches' selection processes have the potential to contribute to the manifestation of RAE. A sequential multi-method research typology was adopted to gain a richer, more contextualized understanding of RAE. Results revealed that RAE was evident in all age groups of Welsh junior club rugby from ‘Under 7–19 yrs'. Odds ratios showed that the magnitude of the RAE increases with the three levels of performance (district, regional and national) above the club game. Further, the process of selection had characteristics that increased the risk of RAE occurring, especially a propensity to use physical characteristics as the primary selection criteria when selecting for representative teams. Also, coaches' overemphasis on game performance and winning appeared to determine that the older, potentially bigger, faster, stronger players are preferred over the younger less physically mature players.

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)0.004
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 score0.089
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.373
Teacher spread0.335 · 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

Citations43
Published2015
Admission routes1
Has abstractyes

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