La création musicale environnementale productrice de sensibilité éco-esthétique vis-à-vis des paysages sonores urbains
Bibliographic record
Abstract
The composer and theorist Murray Schafer proposed the concept of soundscape, which transformed our understanding of our place in the world by questioning the extent to which sound can influence our relationship with our environment. The distinction made by Schafer and his colleagues between hi-fi and lo-fi soundscapes leads to a bias against urban spaces, whose significant activity is primarily viewed as a form of noise pollution. The focus then shifts to reducing this urban activity rather than exploring its aesthetic potential. This results in a divide between humanity and nature, which are then considered two distinct entities rather than two elements of a unified whole. We can therefore ask how the creation of environmental music, an aesthetic movement offering pieces composed from field recordings, might generate a new eco-aesthetic sensitivity towards urban soundscapes that would bridge the divide between humanity and nature? I propose to approach this issue through the lens of Guattari's ecosophy, using a research-creation approach that involves a dual immersion: in the creative process and in the landscape. My objectives are: 1) to collect sounds in an urban space; 2) to conduct a typological analysis based on parameters defined by the WSP (World Sound Project) to understand their perceptual and cultural roles in the space; 3) to create works of environmental music representative of the compositional practices of this movement; and 4) to manifest Guattari's ecosophy through the production and listening of environmental music.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".