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Record W2693611828

A proposed theoretical model for RAEs in sport

2014· article· en· W2693611828 on OpenAlexaff
Nick Wattie, Jörg Schorer, Joseph Baker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsYork UniversityOntario Tech University
Fundersnot available
KeywordsVariety (cybernetics)SalientPsychologyMacroPerspective (graphical)Cognitive psychologyEconometricsComputer scienceMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Relative age, the differences in age between peers within a cohort, has been found to influence a variety of developmental outcomes (i.e., relative age effects). In sport, relatively older youth typically experience advantages, such as a greater likelihood of being selected to sports teams. Relative age effects have been repeatedly identified in a variety of sports, in athlete samples internationally, and in samples ranging from recreational youth participants to elite adult. However, this research, while important, has been described as somewhat atheoretical (Cobley et al., 2009). As such, we propose a theoretical framework that is evidenced-based and consistent with findings in this area. The foundation of this theoretical model is derived from Newell’s model of constraints (Newell, 1986) and the proposition that outcomes emerge from the interaction between individual constraints (related to the performer), task constraints (related to the demands and rules of the activity) and environmental constraints (related to physical and socio-cultural environmental characteristics). Our model also incorporates an epidemiological ‘causal pie’ structure (Rothman, 2002) wherein the specific salient constraints are depicted so that their size (‘pie piece’) is explicitly related to the magnitude of their influence on a RAE. This presentation will outline the evidence that supports the proposed model, and the model’s functional characteristics. In addition, we also present two methodological implications for future research that emerge from the proposed model: macro and micro studies of RAEs. Macro approaches are those that incorporate as broad of a systems-perspective as possible with a multivariate analyses of constraints (individual, task and environmental). Alternatively, micro approaches could test, perhaps even experimentally, the influence and mechanisms of specific constraints. Both approaches may have important implications for advancing this field and for future RAE interventions.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0050.006
Open science0.0040.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0220.003

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 head0.017
GPT teacher head0.288
Teacher spread0.272 · 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 designTheoretical or conceptual
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

Citations0
Published2014
Admission routes1
Has abstractyes

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