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Record W4384915619 · doi:10.23977/aetp.2023.070715

The Left Hand Training Strategy in Piano Performance Teaching

2023· article· en· W4384915619 on OpenAlexvenueno aff
Yuqi Li

Bibliographic record

VenueAdvances in Educational Technology and Psychology · 2023
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsPianoTraining (meteorology)Flexibility (engineering)Performing artsComputer scienceQuality (philosophy)PsychologyMathematicsAcousticsArtVisual arts

Abstract

fetched live from OpenAlex

The training of performance skills is very important in piano learning and performance, which directly determines the performance quality and effect of the performer. As a multi-voice instrument, the piano requires the joint control of the left and right hands to obtain the ideal performance effect. In this regard, the performer should improve the attention to the left hand performance training, and show the beautiful and moving piano performance. Based on this, the paper briefly describes the importance of piano left hand performance, analyzes the existing deficiencies of piano left hand performance training, puts forward the training method of combining learning and thinking, and selects the targeted training method; enrich the left hand performance training materials, form a perfect knowledge system of left hand performance, innovate the left hand performance training mode, improve the performance training system; adhere to the polyphonic piano performance training, exercise the flexibility of the left hand finger, and show the overall effect of piano works to the greatest extent.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.036
GPT teacher head0.411
Teacher spread0.375 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2023
Admission routes1
Has abstractyes

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