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Record W2469965914 · doi:10.1177/1012690216654719

How power moves: A Foucauldian analysis of (in)effective coaching

2016· article· en· W2469965914 on OpenAlexafffund
Joseph P. Mills, Jim Denison

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2016
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of AlbertaSt. Mary's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsConfession (law)CoachingDisciplineConceptualizationPower (physics)AthletesPsychologyOrder (exchange)Everyday lifeApplied psychologySociologyComputer sciencePolitical scienceSocial scienceLawPsychotherapistBusiness

Abstract

fetched live from OpenAlex

Knowing how to coach effectively is one ever-present truth across all sports and yet our previous research based on the work of Michel Foucault has illustrated how the effectiveness of endurance running coaches’ everyday coaching practices is limited by their use of various disciplinary techniques. Missing from these analyses was any consideration of Foucault’s conceptualization of how modern power works through the disciplinary instruments or the confession to progress coaches’ practices. To address this gap in this paper, we present data from interviews and observations with 15 male high-performance endurance running coaches in the United Kingdom and the United States to examine how the exercise of disciplinary instruments along with the confession affects endurance running coaches’ understanding of how to coach. In our analysis we show how discipline’s instruments and the confession operate in ways that significantly restrict and limit endurance running coaches’ efforts to develop their athletes and progress their practices. In order to develop effective coaches it is therefore essential that coaches become aware of how power operates in and around their coaching environment.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.378
Teacher spread0.352 · 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.

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

Citations71
Published2016
Admission routes2
Has abstractyes

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