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Record W2160049396 · doi:10.1123/jis.6.2.147

An Examination of the Impact of Relative Age Effects and Academic Timing on Intercollegiate Athletics Participation in Women’s Softball

2013· article· en· W2160049396 on OpenAlexaff
Jess C. Dixon, Vincenzo Liburdi, Sean Horton, Patricia L. Weir

Bibliographic record

VenueJournal of Intercollegiate Sport · 2013
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsQuartileAthletesContext (archaeology)DemographyPsychologyMedicineSociologyHistoryPhysical therapy

Abstract

fetched live from OpenAlex

This investigation makes three noteworthy contributions to literature on the Relative Age Effect (RAE); 1) it adds to the small number of studies in women’s sports, 2) it is one of very few papers to examine the RAE in intercollegiate athletics, and 3) it (re-)introduces “academic timing” to the discussion of RAEs in this context. The 50 top-ranked NCAA Division I women’s softball teams at the conclusion of the 2011 season served as the focus for this investigation. Student-athletes were grouped into quartiles according to their birth date and identified as “on-time” or “academically delayed” based on their birth year and eligibility status. On-time student-athletes were over four times more likely to be born in quartile one than in quartile four, demonstrating a traditional RAE. This pattern was reversed for those who were academically delayed, with quartile four birth dates constituting more than half of the entire sample.

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)0.005
metaresearch head score (Gemma)0.017
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.367
Teacher spread0.341 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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