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Record W2772030532 · doi:10.3968/9881

The Cultural Content and Singing Skills of the National Vocal Music Chang Xiang Zhi

2017· article· en· W2772030532 on OpenAlexvenueno aff
Lan Xiao-wei

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSingingVocal musicAestheticsNatural (archaeology)Relation (database)HistoryLiteratureTraditional musicVisual artsArtMusicalMusicMusic educationAcousticsComputer science

Abstract

fetched live from OpenAlex

The national vocal music Chang Xiang Zhi is a piece of highly valued work in both educational and practical singing. It originated from the collection of Yuefu songs of Han Dynasty and has abundant historical and cultural contents. How to deal with this kind of work that has a close relation with Chinese traditional culture? The singer needs to not only understand its cultural background and characteristics, but also find its origin and grasp the spiritual nature of the work. In respect of singing skills, the singer needs to show the characteristics of natural and pure, simple and elegant, translucent and transparent, light and graceful of the work in the use of acoustic methods and processing techniques, and also needs to carefully study and experience its cultural contents, making his or her voice abound in the lingering charm of the traditional folk music culture aesthetics.

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)0.000
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.191
GPT teacher head0.266
Teacher spread0.075 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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