Tuning into Black Sounds: An Arts-Based Inquiry into Disrupting Post-Secondary Eurocentric Music Education
Bibliographic record
Abstract
Emerging as an arts-based inquiry, my study works to develop a listening practice that foregrounds the ways Black youth musicians in Southern Ontario engage with music as a site of connection, resistance, healing, and futurity. Following the research documenting the persistent dominance of Eurocentric canons in post-secondary music education and the marginalization of Black musical traditions, I argue that these exclusions constrain possibilities for belonging and fail to recognize the richness of Black music knowledge systems. To disrupt this legacy, my project employs narrative inquiry, critical race theory, and autoethnography, while centering creative methods such as song association, playlist creation, and co-listening. In developing this praxis, I engage participant stories and song selections in conversation with broader themes of Black community and unspoken solidarity, presence in artistry, mutual aid, intentional joy, humour, and softness as resistance, and sounds as sites of comfort and healing. By tracing these lived sonic practices, I suggest that this study re-envisions music and sound as living archives that affirm history, nurture collective care, and sustain possibilities for imagining otherwise futures rooted in Black knowledge, community, and creativity.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".