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Record W2129482322 · doi:10.1177/0305735613477180

Music motivation and the effect of writing music: A comparison of pianists and guitarists

2013· article· en· W2129482322 on OpenAlexafffund
Peter D. MacIntyre, Gillian Potter

Bibliographic record

VenuePsychology of Music · 2013
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsCape Breton University
FundersSocial Sciences and Humanities Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCape Breton University
KeywordsPsychologyPianoCompetence (human resources)MusicalAutonomyCognitive psychologySocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study is to examine a set of motivation variables within guitar and piano players. We also tested for motivational differences among three groups: those who write music, those who plan to write music in the future, and those who do not write nor intend to write. An international sample of 599 musicians was obtained (guitar: N = 292, piano: N = 307) through the use of an online survey. Self-Determination Theory, a prominent perspective in the motivation literature, was utilized along with other motivational constructs, including perceived competence, musical self-esteem, effort, desire to learn, willingness to play, and possible musical selves. Findings revealed differences between pianists’ and guitarists’ levels of motivational intensity, desire to learn, introjected regulation, perceived competence and willingness to play. Results also indicated that the group who write music had significantly higher levels of musical self-esteem, willingness to play, motivational intensity, desire to learn, and perceived competence. Findings from this study suggest that pianists and guitarists both are intrinsically motivated, but for different reasons. The underlying motivational needs that are met by the instrument’s “culture” appear to focus on competence for pianists and on autonomy and relatedness for guitarists.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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 designObservational
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

Citations36
Published2013
Admission routes2
Has abstractyes

Explore more

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