"Music is the weapon": Music as an anti-colonial tool for Aboriginal people in Toronto
Bibliographic record
Abstract
This thesis examines how music can function as an anti-colonial weapon. By adopting an Indigenous anti-colonial framework, this thesis critically examines how music as a public medium can transgress dominant, racist narratives and create powerful avenues through which Aboriginal identity can be articulated and experienced by Aboriginal people. Ten formal interviews were conducted with Aboriginal musicians, traditional singers, and behind the scenes music industry people involved with the Aboriginal community of Toronto. Interview candidates included singers from local drum groups, such as Eagle Heart Singers, Red Spirit Singers, Morning Star River, and Spirit Wind. Award winning musicians such as Lucie Idlout, Leela Gilday, and Derek Miller also contributed their insights, as well as the founder of Rez Bluez, Elaine Bomberry. These interviews uncovered that, within Toronto, music is building vital connections to Aboriginal cultures, generating visibility of contemporary Aboriginal identities within public realms, and fostering relationships between Black and Aboriginal communities through musical genres such as blues and hip-hop. Overall, this thesis argues that music has the potential to be a revolutionary tool that Aboriginal people can use to decolonize themselves and their nations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".