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Record W2737340739

Sport psychology, Foucault and athlete docility

2011· article· en· W2737340739 on OpenAlexaff
Joseph P. Mills, James Denison

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2011
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSport psychologyAthletesField (mathematics)ConfusionPsychologySociologyEpistemologySocial psychologyPsychoanalysis
DOInot available

Abstract

fetched live from OpenAlex

The work of Michel Foucault has had a considerable impact on a wide range of academic disciplines but is yet to infiltrate the field of sport psychology. Foucault's work offers rich reward to sport psychology because his primary interest was how human beings acquired knowledge of themselves and their practices. One Foucauldian concept that has considerable potential to contribute to our understanding of sport performance is that of 'docility'. Foucault's (1995) Technologies of Discipline is a theoretical framework that showed how the meticulous organization involved in many of modern societies institutions such as prisons, schools, or the workplace can result in human docility. This is problematic because it may produce behaviours – compliance, apathy, confusion—that make humans more not less predictable. After outlining this framework we illustrate how these organizational structures have saturated modern sporting practices. Specifically, the combination of the organization of time, space and movement are techniques that Foucault (1995, p. 139) referred to as small acts of cunning. We argue that they produce a host of smaller, less visible and taken-for-granted practices that instill a discipline on athletic bodies that may ultimately undermine athlete performance. Sport psychology practitioners may gain better results with athletes through a greater awareness of how modern sporting practices – designed with efficiency in mind, may actually hinder outstanding individual performance.

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 score0.044
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.323
Teacher spread0.283 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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