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Record W2168818982 · doi:10.1080/17511321.2012.745588

Bergson and Athleticism

2012· article· en· W2168818982 on OpenAlexaff
Geoffrey D. Callaghan

Bibliographic record

VenueSport Ethics and Philosophy · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical and Theoretical Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPossession (linguistics)Action (physics)PerceptionSubject (documents)EpistemologyField (mathematics)Relevance (law)PsychologyCognitive sciencePhilosophyComputer scienceLawPolitical science

Abstract

fetched live from OpenAlex

The work of Henri Bergson has gone almost completely unnoticed in philosophy of sport literature. This in no way indicates the level of relevance his programme may carry for the subject. Many of the entrenched debates that have historically helped to shape the field are mirrored by Bergson's own concerns regarding perception and skill acquisition. As such, a thorough study of how the Bergsonian programme might approach the topic of athletic action is in no wise an idle pursuit – in fact, very much the opposite. My intention in this paper is twofold: first, to indicate the natural commerce that exists between Bergson's philosophy and the philosophy of sport; second, and perhaps more ambitiously, to demonstrate that his approach to perception and action not only anticipates, but in some cases may help to edify, certain unresolved issues within the field. The paper develops in three parts. In part I, I provide a brief summary of Bergson's theory of perception as it is developed in Matter and Memory (1896). Parts II and III will apply that theory to two of the central aspects of human motor activity: in part II, I investigate what it is to be in possession of skilled motor behaviour – to make that behaviour ‘automatic’, as it were; in part III, the controversial subject of what it is to acquire and modify skilled motor behaviour will be examined.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.771

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.260
Teacher spread0.195 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractyes

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