Listening to Indigenous Knowledge of the Land in Two Contemporary Sound Art Installations
Bibliographic record
Abstract
This essay addresses the silences and soundings of Rebecca Belmore's (Anishinaabe) and Julie Nagam's (Anishinaabe/Métis/German/Syrian) sound art, which reflects their environmentalism and profound commitment to Indigenous ways of knowing, making, and listening. Working at the intersection of sound art and politics, the two perform sonic interventions into settler colonial spaces—the National Parks system and the gallery, respectively. Belmore's Wave Sound (2017) and Nagam's Our future is in the land: If we listen to it (2017) illustrate how their sound art gravitates toward the ecological and considers what healthy and unhealthy relationships between humans and the nonhuman world—plants, animals, resources—sound like. Belmore and Nagam introduce marginalized perspectives and voices to address the problematic authority of whiteness that conspicuously dominates the discourse on music, sound, and environment—a relatively homogenous and exclusionary artistic, technological, and scientific discussion.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.039 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".