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Record W4210871438 · doi:10.1177/02557614211066342

Children’s feelings about piano performances across a year of study

2022· article· en· W4210871438 on OpenAlexafffund
Charlene Ryan, Hélène Boucher, Gina Ryan

Bibliographic record

VenueInternational Journal of Music Education · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversité du Québec à MontréalToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFeelingPianoPsychologyContext (archaeology)Developmental psychologyPerceptionSocial psychologyArt

Abstract

fetched live from OpenAlex

Solo performance is a common experience for children learning to play an instrument, yet the research literature on these experiences is limited, with a focus on older children and adolescents. The purpose of this study was to examine younger children's feelings about performance over the course of a year of study. Forty-one children were interviewed about their piano lessons and performance experiences at the end of two consecutive semesters of study. They also responded to a pictorial scale on their feelings about performance at each interview and again at two piano recitals. Results indicate that children are remarkably consistent in their feelings about performing in piano recitals, with few significant changes over time and context. Correlation analyses indicate changes in the relationships between feelings about performance and certain study variables over time-in particular age, liking of lessons, liking of performing, practice time, and perception of being good at piano. In the fall term, gender and age are significant predictors of feelings about performance, with younger children and boys feeling most positive. In the spring, the findings shift and the only significant predictor is children's liking of piano lessons. Implications and directions for further research are discussed.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.994

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.291
Teacher spread0.259 · 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 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

Citations7
Published2022
Admission routes2
Has abstractyes

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