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Record W7005157678

A performance guide: new cello compositions by Serra Miyeun Hwang

2017· dissertation· en· W7005157678 on OpenAlexaboutno aff

Bibliographic record

VenueIowa Research Online (The University of Iowa) · 2017
Typedissertation
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionHyporeflexiaWindageDysgeusiaTubulopathyTSG101
DOInot available

Abstract

fetched live from OpenAlex

Korean Canadian composer Serra Miyeun Hwang (1962-) has written three compositions for cello- Beckoning, Presence, and Hundredth View- inspired by Korean culture and traditional music. She infuses each piece with Korean elements, including special rhythmic patterns, text, and tone color, which are influenced and motivated by traditional Korean percussion music, religions, culture, combined with techniques of Western instrumental performance. The purpose of this study is to introduce Hwang’s music to other cellists and help them incorporate the historical and cultural aspects of Korean traditional music to their performance practices. By analyzing Hwang’s compositions in greater detail, this essay will provide cellists practice guidelines to achieve the desired tone and interpretive gestures of new cello repertoire.\nThis essay contains Hwang’s biography and her philosophy of music and a description of the Korean influences on her music, including the genres of traditional music in Korea, their cultural background, music in Shaman ceremonies, Buddhist music, and p’ungmul (folk drumming and dance). There is also a performance guide of Beckoning for Cello and two Korean Drums; Presence for Soprano, Cello, and Piano; and Hundredth View for Solo Cello with my own interpretation.\nLearning Hwang’s pieces will bring cellists new experiences that are a mixture of music, culture, thoughts, and methods from Western and Eastern influences.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.998

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.290
Teacher spread0.267 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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