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Record W2200679485 · doi:10.1177/1747954115624824

Exploring coach behaviours, session contexts and key stakeholder perceptions of non-linear coaching approaches in youth sport

2016· article· en· W2200679485 on OpenAlexaff
Don Vinson, Abbe Brady, Ben Moreland, Niall Judge

Bibliographic record

VenueInternational Journal of Sports Science & Coaching · 2016
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoachingPsychologySession (web analytics)Psychological interventionSituatedElitePerceptionApplied psychologyTriangulationStakeholderQualitative researchQualitative propertyPedagogySociologyPublic relationsComputer science

Abstract

fetched live from OpenAlex

Gaining better understanding of coaching pedagogies remains a crucial aspect of developing practice. In particular, pedagogic strategies which do not follow transmission-based, technically focused, approaches have been under-investigated. Furthermore, most investigations into coaching processes have elicited an incomplete understanding of the respective pedagogies due to deficiencies in the methodology such as limited triangulation of methods. This study utilises two systematic observation instruments, field notes, individual coach interviews and parent group interviews in order to investigate non-linear coaching pedagogies in three youth sport environments. The systematic observation instruments revealed a lower rate of coach behaviour than has previously been reported alongside fewer technical interventions and more questioning. The qualitative data revealed three themes; creating an environment of participant centredness, holistic development and authentically situated learning. The methodology effectively elicited understanding of the coaches’ pedagogic strategies. Future research should utilise such methodologies to investigate other sporting environments such as in elite and disability sport, particularly studying those approaches which feature non-linear pedagogies.

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)0.003
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 score0.041
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
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 head0.176
GPT teacher head0.362
Teacher spread0.185 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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