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Record W3138640218 · doi:10.1007/978-3-030-65617-1_11

Where Does Diversity Go Straight? Biopolitics, Queer of Color Critique, and Music Education

2021· book-chapter· en· W3138640218 on OpenAlexaff
Elizabeth Gould

Bibliographic record

VenueLandscapes: the arts, aesthetics, and education · 2021
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQueerGender studiesDiversity (politics)SociologyWhite (mutation)Context (archaeology)Queer theoryNeoliberalism (international relations)HeteronormativityAestheticsSocial scienceAnthropologyArtHistory

Abstract

fetched live from OpenAlex

Abstract Diversity discourses in music education have evolved from (white) liberalism of the 1990s that conceived difference in terms of dualisms such as insider/outsider to global neoliberalism currently in which sources of difference are interchangeable as long as the historicity of each remains occluded. In this way, so-called “diversity-relevant” groups, such as white queer people are positioned against non-white groups, straight or otherwise, in ways that support neoliberalism and contribute to violence against the latter. To ask where diversity goes straight assumes a place where it is not straight—if not exactly queer, with queer understood (in the context of race) as a “refusal to inherit” kinship relations in which queer(s) disappear(s). Whether conceived in terms of culture, race, (dis)ability, gender, and/or sexuality, diversity has become “all the rage” in music education and academic research generally. Theorizing diversity discourses in music education at their discursive limits, I argue that those limits are also where they also may be exceeded and demonstrate this through an example using queer of color critique to analyze interactions of sources of difference as a way to historicize and racialize “diversity” in 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.816
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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.234
Teacher spread0.209 · 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
GenreOther

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

Citations2
Published2021
Admission routes1
Has abstractyes

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