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Record W7034655206

Towards a (Self-)Compassionate Music Education: Affirmative Politics, Self-Compassion, and Anti-Oppression

2020· article· en· W7034655206 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage, Communication, and Linguistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsColonialismPower (physics)State (computer science)Context (archaeology)White (mutation)
DOInot available

Abstract

fetched live from OpenAlex

In Red Skin, White Masks: Rejecting the Colonial Politics of Recognition, Glen Coulthard argues that since 1969, colonial power relations in Canada have shifted from an unconcealed structure of domination to a mode of colonial governance that operates through state recognition and accommodation. He instead looks to identify a type of recognition based on self-affirmation and self-recognition rather than state acceptance. Following Coulthard, I examine movements created to affirm oppressed groups in the context of anti-Semitism and anti-Blackness in the mid-twentieth century and explore possible limitations of such movements, including the erasure or elision of complex intersections of identity. I then draw upon self-compassion, a mental health and wellness approach, as a potential framework for the affirmative politics Coulthard theorizes. Subsequently, I consider whether such a framework offers a mechanism to provide the self-affirmation and recognition that Coulthard identifies as vital to resisting oppression. I ultimately explore how understanding music as cultural production in music education might engender this affirmative politics to facilitate rich affirmation and validation of students and educators to musically imagine different possible futures.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0090.032
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.029
GPT teacher head0.277
Teacher spread0.248 · 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 designTheoretical or conceptual
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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueProject Muse (Johns Hopkins University)Same topicLanguage, Communication, and Linguistic StudiesFrench-language works237,207