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Record W4413042890 · doi:10.5539/ach.v17n2p15

Kunqu Opera Training be Beneficial to Contemporary Actor

2025· article· en· W4413042890 on OpenAlexvenueno aff
Yunlin Xiang

Bibliographic record

VenueAsian Culture and History · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicTheatre and Performance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStanislavski's systemAestheticsOperaFeelingSociologyVisual artsConventionArtPsychologySocial scienceSocial psychology

Abstract

fetched live from OpenAlex

The ancient Chinese theatre system was described in Xian Qing Ou Ji by Li Yu (1611-1680), and can be translated as ‘Speaking honestly, if an actor wants to perform a role, it means that the actor speaks instead of the inside thought, so the actor must put their heart into the role to be real during the performance. Actors need to be in the scene personally and there should be true feeling in it.’ The actor should convey their emotions to the audience and must put their whole heart into the character’s background to show their imagination on the stage; this is also an important theory by Stanislavski. From ancient to contemporary theatre theory, and from Chinese theatre to the European theatre, both imagination and physical techniques are crucial to performance. Kunqu opera’s performance convention and formula also may improve actors’ performance techniques. This research integrated Chinese theatre and Europe theatre practice for further expansion and development. Actors’ skills relate to their imagination and physical movement. This research has enriched existing universal performance training methods, and the significance proves the Kunqu opera training be beneficial to contemporary actor’s physical.

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)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.054
GPT teacher head0.239
Teacher spread0.186 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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