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Record W1518061725

Music Makes You Smarter: A New Paradigm for Music Education? Perceptions and Perspectives from Four Groups of Elementary Education Stakeholders.

2011· article· en· W1518061725 on OpenAlexaffabout
John L. Vitale

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsNipissing University
Fundersnot available
KeywordsMusic educationParadigm shiftPerceptionPedagogyFocus groupPsychologyPrincipal (computer security)SociologyMathematics educationComputer scienceEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Through 14 years of teaching music in the Greater Toronto Area, the “music makes you smarter” notion has imbued many of the conversations I have had with multiple stakeholders in public education. Such conversations have suggested that the ancillary benefits of teaching music have now become the principal reason why we teach music-- what I refer to as a new paradigm shift in music education. This study attempts to validate my own experiences through a sample size of 100 participants and a multiple methods approach to inquiry. Specifically, this study explores the perceptions and perspectives of the “music makes you smarter ” notion by four groups of stakeholders in elementary education, namely; music teachers, students, parents, and non-music teachers. With a few exceptions, both quantitative and qualitative data have generated perceptions and perspectives that validate the “music makes you smarter ” notion, suggesting that my own experiences of a paradigm shift within music education in the GTA were indeed authentic and valid. This paper ends with a discussion on the ramifications of this new paradigm shift and ultimately argues that the music itself should be the focus of music education.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.997

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0430.000

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 head0.213
GPT teacher head0.260
Teacher spread0.047 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

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