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Record W2150948952 · doi:10.1177/1012690212462832

Relative age effect in lower categories of international basketball

2012· article· en· W2150948952 on OpenAlexaboutno aff
Miguel Saavedra García, Óscar Gutiérrez Aguilar, Juan J. Fernández Romero, David Fernández Lastra, Gabriel Eiras Oliveira

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2012
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballAthletesDemographyQuarter (Canadian coin)PsychologyMedicineGeographyPhysical therapy

Abstract

fetched live from OpenAlex

To be able to value the relative age effect in the male and female World Championships played between 2005 and 2010 in the U17 categories (athletes 17 years or younger), U19 (athletes 19 or younger) and U21 (athletes 21 years or younger) a sample of 954 players has been selected. The variables registered were their dates of birth, the category of the competition, gender, height and official statistics of each player obtained from the International Basketball Federation (FIBA). A clear relative age effect was found (in both male and female categories) fading with age, being higher in the U17 category, slightly less but also significant in the U19, and no significant effect found in U21. This effect persists when the different specific positions were analysed in the male categories, being clearer in the positions that require more physical strength. In female categories the results do not back the existence of the relative age effect. Also, differences were found in height in the male category with regard to the players’ year-quarter of birth, but its interpretation is not consistent with the relative age effect. In the female category no differences were found in height. Finally, the performance difference of the players in the male and female categories hardly varies with regard to the year-quarter of birth.

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)0.001
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 score0.036
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.028
GPT teacher head0.359
Teacher spread0.331 · 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

Citations45
Published2012
Admission routes1
Has abstractyes

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