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Record W2964519619 · doi:10.18573/mas.83

Timing in Bruce Lee’s Writings as Inspiration for Listening Musically to Hand Combat and Martial Arts Performance

2019· article· en· W2964519619 on OpenAlexaboutno aff
Colin P. McGuire

Bibliographic record

VenueMartial Arts Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMartial artsMusicalVocabularyVisual artsPsychologyAestheticsArtLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Timing is how we know when to do something in order to achieve an aim, and it is essential to all manner of human endeavour. In his posthumous international bestseller Tao of Jeet Kune Do [1975], Bruce Lee discussed timing as a quality of martial arts. His most influential timing concept is broken-rhythm, which is an idea that has resonated with martial artists around the world. Notwithstanding Tao of Jeet Kune Do’s impact, the strategies, tactics, and methods of timing remain poorly expressed in hand combat discourse. That is not to say that martial artists have poor timing, but rather that most martial artists are not very good at explaining how exactly they time their actions. Lee’s own choice of vocabulary was eclectic, drawing from music, fencing, chess, and military drill, which allowed him to discuss diverse approaches to combat time but also led to inconsistencies that muddy the waters for those wishing to engage with his ideas. This article takes up the question of timing in two ways. First, I re-interpret Bruce Lee’s ideas about the rhythm of combat using music theory, which provides precise, self-consistent vocabulary for the task. Second, I explore the meanings that a musical hearing of hand combat reveals at the intersection of sound and movement. Based on extensive fieldwork at a Chinese Canadian kung fu club, I identify some of the ways that percussion-driven performances of choreographed fighting skills have overlooked value as combat training.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.716

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.079
GPT teacher head0.394
Teacher spread0.315 · 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 designNot applicable
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

Citations16
Published2019
Admission routes1
Has abstractyes

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