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Record W2904912004 · doi:10.3390/sports6040167

The Constituent Year Effect: Relative Age Disparities in Australian Masters Track and Field Athletic Participation

2018· article· en· W2904912004 on OpenAlexaff
Nikola Medic, Jasmine Lares, Bradley W. Young

Bibliographic record

VenueSports · 2018
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAthletesPromotion (chess)CompromiseDemographyTrack and field athleticsMedicinePsychologyGerontologyPolitical sciencePhysical therapySociologySocial science

Abstract

fetched live from OpenAlex

The constituent year effect, a source of relative age disparities, in masters sport has been demonstrated mainly amongst North American samples. Thus, the purpose of this study was to examine whether a participation-related constituent year effect exists among athletes (n = 6492) competing in Australian Masters Athletics competitions between 2000 and 2014. The results indicated that a participation-related constituent year effect was observed as the likelihood of participating was significantly higher for masters athletes in their first and second constituent year of any five-year age category (p < 0.0001) and was lower when they were in the fourth or fifth constituent year. The results also indicated this effect is influenced by gender and age. Specifically, the effect was significant for both male (p < 0.0001) and female (p < 0.001) masters athletes; as well during the third, sixth, seventh, and eighth + decades of life (all ps < 0.001). These data demonstrate that despite masters sport being an avenue for promotion of participation and overall health, there is potential for improving how competitive organizational strategies are implemented given the recurring intermittent patterns of participation associated with five-year age brackets which are likely to compromise benefits.

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 score0.024
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 studies0.0000.000
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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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