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Record W2997867865 · doi:10.33137/ijournal.v5i1.33470

A Thousand Times Do it: Historical European Martial Arts and the Cultural Record

2020· article· en· W2997867865 on OpenAlexvenueno aff
David Lichty

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMartial Arts: Techniques, Psychology, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMartial artsPeriod (music)Visual artsThe artsMetadataHistoryArtAestheticsWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

The following paper explores how intangible cultural knowledge is represented in the cultural record as information made known through physical objects. It seeks to prove that the preservation of intangible knowledge requires the continued practice of that knowledge as well as the creation of physical information. I believe that a study of European martial arts will demonstrate this. This paper will cover the history of historical European martial arts manuscripts in the early medieval period, relevant advances in manuscript making in the late medieval period, and the current revitalization of historical European martial arts. The paper will describe how communities of practitioners dedicated to recording guidelines for what can only be realized in practice and which is intangible knowledge, employ new technologies, ideas and metadata in creating a cultural record.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.010
Scholarly communication0.0130.010
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.044
GPT teacher head0.350
Teacher spread0.307 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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