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Record W2040345216 · doi:10.1177/0255761407088487

Educating professional musicians: lessons learned from school music

2008· article· en· W2040345216 on OpenAlexaffabout
Glen Carruthers

Bibliographic record

VenueInternational Journal of Music Education · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsBrandon University
Fundersnot available
KeywordsMusic educationCurriculumSingingRelevance (law)PedagogyPsychologyLimitingManagementEngineeringPolitical science

Abstract

fetched live from OpenAlex

Music in Canadian schools at one time focused on skills development. Building on talent, aptitude, prior learning and physical coordination, students would become better at singing or playing an instrument by studying it at school. Over time, new approaches to music teaching and learning opened the umbrella to a more comprehensive range of objectives. Human understanding, social relevance, the ability to work and play together, and many other outcomes are now typically expected of school music programmes. The present article sets this pedagogical model against the outcomes expected of university music programmes, especially in performance. While human understanding, social responsibility and the like may be acknowledged as desirable outcomes, most university performance programmes focus on developing human capital — on creating better musicians — who may or may not be better people for it. University performance programmes still intend a kind of learning that was long ago deemed limiting, restrictive and ultimately inappropriate for public school curricula. This investigation posits a new approach to music at the university level, with new pedagogical aims, that takes as its starting point innovative and challenging developments in school 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)0.001
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0240.001

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 head0.164
GPT teacher head0.334
Teacher spread0.170 · 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 designNot applicable
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

Citations35
Published2008
Admission routes2
Has abstractyes

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