The Power of Indigenous Music: Ethnography, Resistance, and Protests
Bibliographic record
Abstract
“The Power of Indigenous music: Ethnography, Resistance, and Protests” is a comprehensive study of the history of musical ethnography within the Indigenous topic, how it later affected Indigenous peoples, and how it is shown in popular music today. Throughout the era of musical ethnography, musical salvaging, and corrupted motives took place, and as a result, ethnography was an ever-changing field of anthropology. This history later encouraged Indigenous artists to express themselves in music and other art forms. The Hallcui Nation is used as a ‘case study’ to showcase how Indigenous artists have used various forms of art to express, resist, and protest issues within Indigenous communities caused by colonialism, while an in-depth understanding of ethnography in the musical topic is analyzed. The Halluci Nation is a Hip-Hop based group and their music is focused primarily on protest and resisting while educating their listeners on topics such as police brutality, colonialism, and topics on their communities and ways of understanding. Understanding this history is necessary, and is especially important in school systems of all grades. Within the institution of Queen’s University, and more specifically in the Bachelor of Music program, students are given an opportunity to learn about the music of all cultures and traditions, but usually, only specific cultures and traditions are centered in these discussions. Indigenous artists and musicians create powerful, artistic, soulful, and vulnerable pieces, and to truly understand how powerful they are one must understand the history of colonialism and musical ethnography in Canada.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| 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".