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Beyond Toleration—Facing the Other

2016· book-chapter· en· W2489752171 on OpenAlexaff
Richard Matthews

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsWestern University
Fundersnot available
KeywordsOppressionTolerationReflexivityCriticismPrivilege (computing)SociologyGender studiesPoliticsTheme (computing)Dominance (genetics)RacismAestheticsCriminologyEnvironmental ethicsPolitical scienceLawSocial sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This essay seeks to integrate themes raised by the various contributors to this section of the Handbook. The central issue concerns the role played by music education in creating, perpetuating, or intensifying privilege and oppression. Intersectional considerations are used to show how chapters fit into a larger analysis of violence by exploring specific axes of oppression—for instance, race or gender—and how they relate to a larger situation of unjust social dominance. The concept of structural violence is crucial here, as much of the damage inflicted has direct physical and psychological consequences and either undermines or outright destroys the educational environment for those harmed by it. An essential related theme is the necessity for moral and political self-criticism—reflexivity—in struggling against oppression so that music education is not condemned to perpetuation. There are extraordinary possibilities for resistance available to reflexive music educators.

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.044
Scholarly communication0.0150.015
Open science0.0020.013
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0090.002

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 head0.050
GPT teacher head0.197
Teacher spread0.147 · 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 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

Citations30
Published2016
Admission routes1
Has abstractyes

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