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Record W2804921019 · doi:10.31091/lekesan.v1i1.341

Meguru Panggul and Meguru Kuping; The Method of Learning and Teaching Balinese Gamelan

2018· article· en· W2804921019 on OpenAlexaboutno aff
I Wayan Sudirana

Bibliographic record

VenueLekesan Interdisciplinary Journal of Asia Pacific Arts · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsHumanityMusicalProcess (computing)PerceptionStyle (visual arts)Music educationPedagogyVisual artsSociologyAutoethnographyPsychologyRealization (probability)Mathematics educationAestheticsArtComputer scienceSocial science

Abstract

fetched live from OpenAlex

“True musical experience is the experience of trust”—trust between the student and teacher. Whatever teaching method a teacher applies, it will not work without any trust. “It is only when we learn to trust one another, to dissolve in the realization of our shared humanity, will the music finally play.” This is an autoethnography. It exhibits the long process of musicianship in a traditional Balinese community. Also, I explore how, as a modern Balinese musician, my musicianship fit in with the new musical setting of a Western community. The paper is divided into three parts: the first part is an exploration of the traditional learning process and Balinese musical pedagogy called meguru panggul. The second is an exploration of my experience in continuing my studies at ISI Denpasar (the Balinese Arts Institute)— how the teacher conducts the learning process in a formal setting, and my own discovery in learning with ear (meguru kuping). And lastly, the third explores the development of my perception and conception of a new learning and teaching style, when I was exposed to the Western way of teaching and learning music at the University of British Columbia, Vancouver, Canada.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.316
Teacher spread0.284 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

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