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Record W2115512550 · doi:10.1080/02640410903582743

Knowing what to do and doing it: Differences in self-assessed tactical skills of regional, sub-elite, and elite youth field hockey players

2010· article· en· W2115512550 on OpenAlexaff
Marije T. Elferink‐Gemser, Rianne Kannekens, Jim Lyons, Yvonne Tromp, Chris Visscher

Bibliographic record

VenueJournal of Sports Sciences · 2010
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEliteAthletesPsychologyField hockeyElite athletesApplied psychologyPhysical therapyMedicinePolitical scienceFootball

Abstract

fetched live from OpenAlex

To determine whether youth athletes with an "average" (regional), "high" (sub-elite), and "very high" (elite) level of performance differ with respect to their self-assessed tactical skills, 191 youth field hockey players (mean age 15.5 years, s = 1.6) completed the Tactical Skills Inventory for Sports (TACSIS) with scales for declarative ("knowing what to do") and procedural ("doing it") knowledge. Multivariate analyses of covariance with age as covariate showed that elite and sub-elite players outscored regional players on all tactical skills (P < 0.05), whereas elite players had better scores than sub-elite players on "positioning and deciding" (P < 0.05) only. The sex of the athletes had no influence on the scores (P > 0.05). With increasing level of performance, scores on declarative and procedural knowledge were higher. Close to expert performance, declarative knowledge no longer differentiated between elite and sub-elite players (P > 0.05), in contrast to an aspect of procedural knowledge (i.e. positioning and deciding), where elite players outscored sub-elite players (P < 0.05). These results may have implications for the development of talented athletes.

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 score0.034
Threshold uncertainty score0.378

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 communication0.0000.001
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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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

Citations58
Published2010
Admission routes1
Has abstractyes

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