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Record W3005638877 · doi:10.15273/jue.v10i1.9946

Karate-Talk in a Canadian Dojo

2020· article· en· W3005638877 on OpenAlexvenueaboutno aff
Michaela Peters

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMartial artsIdentity (music)Context (archaeology)SociologyLinguisticsCode (set theory)Visual artsHistoryAestheticsComputer scienceArtPhilosophy

Abstract

fetched live from OpenAlex

Karate-do is one of many budo, or martial ways, that originated during the Kamakura Shogunate of Japan. The original dojos (training halls), used the Japanese language to indoctrinate karate students into the moral code of the dojo community. Over the last century karate has spread across the world, and other languages have been combined with Japanese to teach the art and sport. In this article, the discursive practice of combining English and Japanese in Canadian dojos is called Karate-Talk. Using identity frameworks from linguistic anthropology and sociolinguistics, I illustrate and interpret how Karate-Talk teaches students the moral and ethical codes that are embedded in karate training, and in doing so helps students develop their black belt identity. Dojos want their students to develop black belt identities because it helps to pass on the traditions of karate-do and contributes to the preservation and continuation of the art form. This article describes Karate-Talk in a socio-historic context, and establishes the ways it is used to create black belt identities in karate students through the use of a case study.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.081
GPT teacher head0.396
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

Citations3
Published2020
Admission routes2
Has abstractyes

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