Reclaiming Oral Knowledge: Indigenous Classical Musicians’ Decolonial Approaches
Bibliographic record
Abstract
Since the nineteenth century, ethnographers and ethnomusicologists have collected Indigenous cultural materials across Turtle Island, archiving them in museums and government spaces. This paper supports the argument that archiving does not ‘preserve’ the music but prevents traditional and wholistic ways of living and learning by undervaluing oral knowledge. In turn, archival materials become static. However, due to the attempted erasure of Indigenous culture, these archival materials have become more valuable to Indigenous communities who seek to reclaim and decolonize them within the archives today. Through an analysis of the creative processes of Indigenous musicians Jeremy Dutcher and Cris Derksen this paper argues that these artists’ work decolonize and legitimize oral knowledge through their compositional processes. Dutcher and Derksen’s work challenge the preconceived thought that oral traditions cannot be a trusted source of knowledge that has been passed down by Indigenous communities from generation to generation.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.073 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".