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Record W2145457913 · doi:10.1260/174795409789623883

Formal vs. Informal Coach Education

2009· article· en· W2145457913 on OpenAlexaff
Clifford J. Mallett, Pierre Trudel, John Lyle, Steven Rynne

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2009
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoachingAccreditationScholarshipInformal learningBeijingInformal educationPedagogyFormal learningFormal educationPerspective (graphical)PsychologyMedical educationSociologyHigher educationPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The training of coaches is considered central to sustaining and improving the quality of sports coaching and the ongoing process of professionalisation. Sports coaches participate in a range of learning opportunities (informal to formal) that contribute to their development to varying degrees. In this article, we present our collective understanding on the varying types of learning opportunities and their contribution to coach accreditation and development. The authors presented these views (from a sports pedagogy perspective) as part of a workshop entitled “Formal vs. Informal Coach Education” at the 2007 International Council of Coach Education Master Class in Beijing. These reflections seek to stimulate the on-going, and often sterile, debate about formal versus informal coach education/learning in order to progress scholarship in coaching.

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)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.014
GPT teacher head0.358
Teacher spread0.344 · 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 designQualitative
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

Citations328
Published2009
Admission routes1
Has abstractyes

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