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Record W1501526287 · doi:10.1111/sms.12387

Sibling dynamics and sport expertise

2015· article· en· W1501526287 on OpenAlexaff
M. J. Hopwood, Damian Farrow, Clare MacMahon, J. Baker

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2015
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsYork University
Fundersnot available
KeywordsDynamics (music)SiblingPhysical therapyMedicinePsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Family members are known to be highly influential in the development of sport expertise. To date, much of the research in this area has focused on parents, with less known about sibling influences on expertise. This investigation explored associations between sport expertise, sibling characteristics, and sibling participation in sport and physical activity. Athletes representing three skill levels provided details of sibling characteristics and participation in sport and physical activity via the Developmental History of Athletes Questionnaire. Elite athletes were more likely to be later-born children, while pre-elite and non-elite athletes were more likely to be first-born. Compared with siblings of non-elite athletes, siblings of elite athletes were more likely to have participated in regular physical activity and were more likely to have participated in sport at the pre-elite and elite levels. These results suggest siblings may play a key role in sport expertise development.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.045
GPT teacher head0.346
Teacher spread0.301 · 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

Citations48
Published2015
Admission routes1
Has abstractyes

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Same venueScandinavian Journal of Medicine and Science in SportsSame topicMotivation and Self-Concept in SportsFrench-language works237,207