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Record W2951895535 · doi:10.1123/japa.2018-0439

The Constituent Year Effect in European Track and Field Masters Athletes: Evidence of Participation and Performance Advantages

2019· article· en· W2951895535 on OpenAlexaff
Werner Helsen, Nikola Medic, Janet L. Starkes, A. Mark Williams

Bibliographic record

VenueJournal of Aging and Physical Activity · 2019
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAthletesTrack and field athleticsInequalityPsychologyDemographyMedicinePhysical therapyMathematicsSociology

Abstract

fetched live from OpenAlex

Inequalities in relative age distribution have previously been demonstrated to influence participation and performance achievements in Masters athletes. The purpose of the present study was to examine the participation- and performance-related constituent year effect among Masters athletes (N = 2,474) from the European Masters Track and Field Championships across subdisciplines and age. The results indicated that a participation-related constituent year effect was observed. The likelihood of participation was significantly higher for athletes in their first year of any 5-year age category (χ2 = 149.8, p < .001) and decreased significantly when they were in the fourth or fifth year. The results also indicated a performance-related constituent year effect. Masters athletes in their first year won significantly more medals than expected based on observed participation rate (χ2 = 23.39, p < .001). We compare our results with the existing literature and discuss potential mechanisms for this constituent year effect.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.311
Teacher spread0.291 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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