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Record W2883055145 · doi:10.18573/mas.57

Putting the Harm Back into Harmony: Aikido, Violence and ‘Truth in the Martial Arts’

2018· article· en· W2883055145 on OpenAlexaff
William Little

Bibliographic record

VenueMartial Arts Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMartial artsAestheticsContext (archaeology)SociologyEpistemologyArtVisual artsPhilosophyHistory

Abstract

fetched live from OpenAlex

This paper will address the theme of ‘truth in the martial arts’, a phrase from Mitsugi Saotome’s recent reflection on his relationship as Uchi Deshi to Morihei Ueshiba, the founder of Aikido. I will frame this theme sociologically, exploring it as an aspect of the martial arts as contemporary practices of the self. What is distinct about the practice of the martial arts in this context is their sustained reflection on violence, not simply as violent contest, but as a condition of irreducible insecurity per se. I would like to propose that Aikido (not unlike other martial arts) offers a response to violence by articulating a form-of-life – ‘a life that can never be separated from its form’ (Giorgio Agamben) – that is centred on the understanding that complete martial fluidity is immanent to life. The martial arts are therefore very interesting contemporary practices of the self because their paths to knowledge address key biopolitical issues of life and power through a freeing relation to violence. I would also like to propose that the framework of transcendental empiricism, which Gilles Deleuze develops to describe the dynamics of affectual as opposed to representational (i.e. mediated) experience, is both promising to characterize the experience of martial fluidity and to expand the self-understanding martial artists themselves. Martial artists are uniquely positioned to decipher Agamben’s and Deleuze’s theoretical texts because of the deep, embodied knowledge that emerges through practice.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
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 head0.104
GPT teacher head0.420
Teacher spread0.317 · 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 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

Citations18
Published2018
Admission routes1
Has abstractyes

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