Towards a (Self-)Compassionate Music Education: Affirmative Politics, Self-Compassion, and Anti-Oppression
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".