Decolonizing Desires and Unsettling Musicology: A Settler’s Personal Story of Researching and Teaching Indigenous Music at an American University
Bibliographic record
Abstract
For those of us with decolonial desires, the university classroom is a potential space of disruption and reorganization. Our courses, course materials, teaching tools, students, and our own bodies and minds are all technologies that can subvert the colonial machine (la paperson 2017). In the first section, I contextualize my decolonial desires as a non-U.S.-citizen settler Canadian musicologist in the United States. The work of David Garneau, Aileen Moreton-Robinson, Andrea Smith, Eve Tuck, and K. Wayne Yang illuminates my positionality and power. In the second section, I provide an example of one way I’m disrupting the typical curricula and classroom experiences in a Euro-American classical music school. I discuss my course entitled “North American Indigenous Music Seminar” (NAIMS), including the course structure and content, and decolonizing strategies. Student responses to interviews about the course are interspersed with the discussion of my seminar plans and challenges to claims of “decolonization.” Their responses reveal some successes and many limits for anti-colonial and decolonial work in a single-semester course.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.063 | 0.040 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".