Bibliographic record
Abstract
Acoustic ecology has served as a foundational theoretical field for many sound scholars to understand the soundscape as a signifier for environmental crisis. While sound theorists like R. Murray Schafer and those in the World Soundscape Project have developed ways in which to critically analyze environmental soundscapes, these methods have often excluded Indigenous narratives which offer complex understandings of sound through embodied experience. In this paper I employ a brief description of acoustic ecology, drawing attention to its benefits as a methodological approach to sonic ordering, while also demonstrating the possibilities for expansion of this field when examined in conversation with Canadian Indigenous perspectives and notable sonic activist movements. I address how Indigenous knowledge systems, futurisms, art, and activism can provide critical perspectives within the field of acoustic ecology, which lends well to understanding soundscapes of crisis. I identify a few case studies of sonic forward Indigenous environmental movements which include game design by Elizabeth LaPensée, Rebecca Belmore’s Wave Sound sculpture, and the Round Dance Revolution within the Idle No More movement. In sum, this paper works to bridge the work of acoustic ecology and Indigenous sonic movements to encourage a complex and nuanced relationship to sound, and to explore moments for understanding sonic intersections at the forefront of environmental crisis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".