Music as Discourse to Inform Rural Policy: A Case Study of the Mayangna Artistic Initiatives in Nicaragua
Bibliographic record
Abstract
The conflicts that have recently intensified around land access by indigenous communities and extractive industries have highlighted a need for the development of rural policy that incorporates diverse perspectives. In North America, this has been exemplified by issues around the construction of oil pipelines which threaten resources that are considered sacred components of indigenous cultural heritage as well as their means for sustenance. Similarly, in Latin America, indigenous people face insecurity as their environments are taken by corporations and colonists whose practices are less sustainable. Community activists have responded by carrying out sit-ins and artistic and ceremonial performance, hoping to inform the creation of policies or enforce existing ones. From a performance theory and discourse analysis perspective, this presentation considers that such conflicts emerge out of clashing discourses on social justice and the relationship between humanity and nature. It also posits that artistic expression is an embodiment of these perspectives and suggests that an analysis of the discourse of music performance allows stakeholders to identify sustainability principles that are essential to Indigenous peoples. Through an in-depth case study focusing on the current struggle for land in which the Mayangna indigenous communities of Nicaragua are engaged, I argue that artistic performance remains a key example of discourse whereby communities express and concretize relationships. Understanding this process of knowledge exchange is critical for fostering collaborative relationships with Indigenous people. The outcomes of this research will inform rural policy and development that may be applicable to Ontario or other rural development environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| 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".