Performing Change on the Music Festival Stage: Indigenous Popular Music and Audience Engagement
Bibliographic record
Abstract
Festivals have been credited with significant social effects: connecting people, developing audiences, linking emerging with established artists, even encouraging intercultural dialogue and participating in ongoing positive social change. At the same time, the concretization and commodification of Indigenous expressive culture is a risk in festivalized settings. Emerging from dialogue with Indigenous music industry professionals and musicians, this essay explores how music festivals that prioritize Indigenous leadership and attend to internally diverse audiences can strategically choose productive narratives for the groups they serve. While remote collaboration is not new, it became required during the COVID pandemic. With its focus on musician and audience development, the sākihiwē festival in Winnipeg, Canada demonstrates some of the ways in which First Nations, Métis, Inuit, and international Indigenous musicians are reaching audiences in challenging times. Possibilities for audience curation shift online, as do the tools available for listener engagement. Musicians continue to wrestle with questions of addressing stereotypes as well as how to inspire and educate audiences in a festival atmosphere. To these concerns, performers add the manner in which they work with streaming technology, develop professional mentorship with physically distant colleagues, and create connections with online listeners. As uncertainty continues around music festivals in the near future, this essay asks how possibilities are shifting around cultural and political change through music festival performance.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".