MétaCan
Menu
Back to cohort
Record W3004700553 · doi:10.1177/0193723520903228

Breaking Coaching’s Rules: Transforming the Body, Sport, and Performance

2020· article· en· W3004700553 on OpenAlexaff
Joseph P. Mills, Jim Denison, Brian Gearity

Bibliographic record

VenueJournal of Sport and Social Issues · 2020
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsArticulation (sociology)CoachingTransformative learningDisciplinePower (physics)Sociocultural evolutionAthletesSociologyPsychologyEpistemologyUnintended consequencesAestheticsPedagogyLawPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

“Who knew that doing the wrong things could make everything so right?” There can be little doubt that sports’ dominant bioscientific articulation of the athletic body exerts a strong influence on coaches. Yet, on closer examination, this articulation and the practices it produces are not as straightforward as most coaches and scholars assume. Within the sociocultural study of coaching, scholars have drawn on Michel Foucault’s disciplinary framework to analyze many unseen problems and unintended consequences associated with coaches’ normal or everyday (bioscientific) practices. However, one significant aspect of Foucault’s theoretical framework that has received less attention from coaching scholars is how power and discourse work together to produce several coaching “truths.” To address this gap, in this article, we analyze the first author’s experiences as an international middle-distance runner by showing and telling what problems and constraints are produced when coaches, and by association their athletes, defer to a dominant bioscientific articulation of the athletic body in their training. We conclude by discussing the transformative possibilities when these “truths” are broken.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.322
Teacher spread0.295 · 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

Citations39
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sport and Social IssuesSame topicSport Psychology and PerformanceFrench-language works237,207