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Record W4212892763 · doi:10.18061/es.v8i0.8653

Introduction: Beyond Western Musicalities

2022· article· en· W4212892763 on OpenAlexaff
Maya Cunningham, Dylan Robinson, Chris Stover, Leslie Tilley, Anna Yu Wang

Bibliographic record

VenueEngaging Students Essays in Music Pedagogy · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsQueen's University
Fundersnot available
KeywordsFraming (construction)SociologyAccountabilityActive listeningCurriculumEpistemologyAestheticsPedagogyPolitical scienceHistoryArtLawPhilosophy

Abstract

fetched live from OpenAlex

It has become increasingly clear that the way we teach music theory is not only incomplete, it is insufficient, even irresponsible. This introduction elucidates some of the key issues around diversifying and decolonizing music theory classrooms. Following a brief framing of the issues, a triptych of short thought pieces examines the contexts and implications of such questions. Dylan Robinson locates core curricula as a "ground" upon which music programs are built and which might be "given back" to BIPOC scholars to re-define; Anna Yu Wang advocates for classroom methodologies which center diverse modes of listening as foundations for music theorizing; and Maya Cunningham considers how Western music theory systems and training are historically colonial and continue to be driven by cultural and economic bias and inequity. Finally, in an ambitious co-authored work, Chris Stover, Leslie Tilley, and Anna Yu Wang organize, synthesize, and extrapolate from survey responses by twenty-four music scholar-pedagogues to offer a panoramic view on diversifying and decolonizing efforts. The essay addresses challenges, disagreements, goals, and possible ways forward, and through its explorations encourages sustained reflection, accountability, and change.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0450.000

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 head0.051
GPT teacher head0.311
Teacher spread0.260 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEngaging Students Essays in Music PedagogySame topicDiverse Music Education InsightsFrench-language works237,207