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Record W2104352265 · doi:10.1177/0255761407083578

On the value of intrinsic rather than traditional achievement goals for performing artists: a short-term prospective study

2007· article· en· W2104352265 on OpenAlexafffund
Natalie Lacaille, Richard Koestner, Patrick Gaudreau

Bibliographic record

VenueInternational Journal of Music Education · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of OttawaMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyDanceIntrinsic motivationIntrinsic value (animal ethics)Goal orientationSocial psychologyValue (mathematics)Relation (database)Need for achievementCognitive psychologyApplied psychologyDevelopmental psychologyComputer science

Abstract

fetched live from OpenAlex

Educational researchers have examined the effect of achievement goals on student performance, and suggest that both mastery goals and performance-approach goals are beneficial, whereas performance-avoidance goals are harmful. Recent research proposes that these results may not be generalized in the domain of music. The purpose of the present study was to examine the relation of performance approach, performance avoidance, mastery and intrinsic goals to the end-of-year public performance of 129 music, dance and acting conservatory students. Results demonstrated that both types of performance goals (approach and avoidance) were associated with negative emotional outcomes, and that only intrinsic goals were associated with positive performance and emotional outcomes. Mastery goals were unrelated to outcomes in this sample. Intrinsic goals associated with aesthetic expression and enjoyment seemed to be particularly helpful for performing artists.

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.074
GPT teacher head0.304
Teacher spread0.230 · 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 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

Citations56
Published2007
Admission routes2
Has abstractyes

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