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Record W4385241439 · doi:10.22492/issn.2187-4751.2023

The Asian Conference on Cultural Studies 2023: Official Conference Proceedings

2023· paratext· en· W4385241439 on OpenAlexaboutno aff

Bibliographic record

VenueAsian Conference on Cultural Studies official conference proceedings · 2023
Typeparatext
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsnot available
FundersNational Cheng Kung UniversityMinistry of Internal Affairs and CommunicationsMinistry of Health, Labour and Welfare
KeywordsLibrary scienceComputer sciencePolitical scienceMedia studiesSociology

Abstract

fetched live from OpenAlex

Aesthetics and the sense of beauty in Japan occupy a special place and are at the foundation of the Japanese national identity. In Japanese culture, every aspect of daily life can become an aesthetic experience. Objects and rituals have to be both beautiful and functional at the same time. This duet of beauty and functionality is illuminated by the constellation of a number of aesthetic concepts. Concept of MA which is often translated as "space between," "mindful pause," or as a "negative space" could also be understood as "active silence" or "presence of absence." From architecture to literature, painting, calligraphy and culinary arts, MA is everpresent in performing arts, martial arts, and all "meditation in motion" practices. Japanese character for MA is part of such words as world, humankind, space, time, moment, circle of friends and many others. This paper will focus on MA in Chanoyu, commonly known as the Japanese tea ceremony, in particular, its applications to the contemplative pedagogy, the ways of teaching and learning of this discipline. The research is based on my students' diaries, which is one of the components of the "Japanese Aesthetics" course taught in a Canadian university. Though Japanese aesthetic concepts are deeply rooted in the country's cultural fabric, it doesn't mean that they cannot appeal to the tastes of non-Japanese. Moreover, I argue that they can be successfully applied as a methodology in various disciplines, pedagogy in particular.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.003
Science and technology studies0.0130.011
Scholarly communication0.0050.002
Open science0.0050.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.011

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 head0.134
GPT teacher head0.386
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2023
Admission routes1
Has abstractyes

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