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Record W4200173404 · doi:10.32370/ia_2021_12_20

Methods for Forming Polyphonic Hearing in Future Music Teachers

2021· article· en· W4200173404 on OpenAlexvenueno aff
Xinyu Zhou

Bibliographic record

VenueIntellectual Archive · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPolyphonyPianoPiano pedagogyPsychologyOrchestrationTimbreMusicalRealization (probability)Style (visual arts)ImprovisationProcess (computing)Music educationMathematics educationComputer sciencePedagogyVisual artsArtMathematics

Abstract

fetched live from OpenAlex

The article actualizes the need for high-quality piano training future Music teachers. Studied the peculiarities in piano training students from Ukraine and China in the way of differences their experience to perceive and perform polyphony pieces of music of different types and styles. Pointed out that in scientific music-pedagogical literature, methodical recommendations for forming polyphonic hearing in students aren't sufficiently presented in defining the problems. The author described own method for forming polyphonic hearing in students described based on the pedagogical principles: reliance on European music and historical experience; emotional passion; creative reflection; performing self-realization in the process of piano training. Presented pedagogical conditions: outright pedagogical management of development of polyphonic thinking; stimulating students to mastering the skills for performing of musical works; praxeological orientation of studying polyphonic works in the piano learning process. The author presents four groups of methods: research method, motivational and emotional method, method of reflection with art-therapeutic techniques, adapted for performing and articulatory-reproductive methods. The peculiarity of this technique is the integration of individual and group forms of work considering the specifics of students’ basics piano training.

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: none
Teacher disagreement score0.860
Threshold uncertainty score0.996

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)0.0050.000

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 head0.099
GPT teacher head0.324
Teacher spread0.226 · 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
Published2021
Admission routes1
Has abstractyes

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