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Record W2025552722 · doi:10.1260/174795409789623937

Discriminating Skilled Coaching Groups: Quantitative Examination of Developmental Experiences and Activities

2009· article· en· W2025552722 on OpenAlexaffabout
Bradley W. Young, Krista Jemczyk, Kevin Brophy, Jean Côté

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2009
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's UniversityUniversity of AlbertaUniversity of Ottawa
Fundersnot available
KeywordsCoachingClubExcellencePsychologyTalent developmentMedical educationApplied psychologyTrack and field athleticsPedagogyAthletesPhysical therapyMedicine

Abstract

fetched live from OpenAlex

Literature suggests that the pathway to coaching excellence involves progression through incremental skilled coaching groups over extended durations. Critical in this development is the immersion of developing coaches in various domains of engagement and learning over time. Using a retrospective survey, this study quantified the cumulative activities, experiences, and interactions that competitive-stream Canadian track and field coaches experienced in formal coaching education, active coaching experience, mentoring, and former athletic experience domains. Analyses identified critical experiences that discriminated between four incremental skill groups: local club (n = 24), senior club (n = 19), provincial (n =10), and national coaches (n = 18). Results demonstrated that certain measures in each of the domains discriminated between the groups, including years of coaching, interactive hours working with athletes, having more assisting coaches whom one has mentored, and having taken more post-secondary coaching courses. These variables, along with former athletic experience prerequisites, were attached to a preliminary between-group developmental framework.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.360
Teacher spread0.330 · 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

Citations47
Published2009
Admission routes2
Has abstractyes

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