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Record W2061994810 · doi:10.1080/02640414.2010.516270

Academic performance and self-regulatory skills in elite youth soccer players

2010· article· en· W2061994810 on OpenAlexaff
Laura Jonker, Marije T. Elferink‐Gemser, Tynke Toering, James E. Lyons, Chris Visscher

Bibliographic record

VenueJournal of Sports Sciences · 2010
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsMcMaster University
Fundersnot available
KeywordsElitePsychologyVocational educationAthletesPopulationAcademic achievementMedical educationStereotype threatMathematics educationApplied psychologyPedagogySocial psychologyPolitical scienceSociologyMedicinePhysical therapyDemography

Abstract

fetched live from OpenAlex

Although elite athletes have been reported to be high academic achievers, many elite soccer players struggle with a stereotype of being low academic achievers. The purpose of this study was to compare the academic level (pre-university or pre-vocational) and self-regulatory skills (planning, self-monitoring, evaluation, reflection, effort, and self-efficacy) of elite youth soccer players aged 12-16 years (n = 128) with those of 164 age-matched controls (typical students). The results demonstrate that the elite youth soccer players are more often enrolled in the pre-university academic system, which means that they are high academic achievers, compared with the typical student. The elite players also report an increased use of self-regulatory skills, in particular self-monitoring, evaluation, reflection, and effort. In addition, control students in the pre-university system had more highly developed self-regulatory skills than those in the pre-vocational system, whereas no difference was observed within the soccer population. This suggests that the relatively stronger self-regulatory skills reported by the elite youth soccer players may be essential for performance at the highest levels of sport competition and in academia.

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)0.002
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 score0.020
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.293
Teacher spread0.277 · 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

Citations56
Published2010
Admission routes1
Has abstractyes

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