“The spaces of sight and sound” – Containment, feedback, and contingency in R. Murray Schafer´s acoustic ecology
Bibliographic record
Abstract
In the 1970s, the World Soundscape Project (WSP) around the Canadian composer R. Murray Schafer popularised the term “acoustic ecology” to examine specific interrelations of sound, space, perception, and technology. What was implied when Schafer and his colleagues referred to their project as “ecology”? Where is the historic project of acoustic ecology located epistemologically with regard to other theories of art, media, and ecology? It is my contention that the WSP´s work with cybernetic concepts in their media practices offers answers to these questions. Focusing on Schafer´s reception of Marshall McLuhan´s concept of “acoustic space”, this article examines Schafer´s practices through the lens of two media theories: thinking through questions of containment and care the paper analyses how Schafer conceptualises modes of adaptation between hearing and the surrounding world. Schafer´s exercises for modulating perception via “Ear Cleaning” are explored as specific strategies to manage contingency – treating the relationship of environment and perception as something that can and should be changed. With a focus on Schafer´s seemingly contradictory perspective on tape recordings demonstrated how his practices operationalise a technological openness that his theories both rely on and deny. For it is totality, not contingency that Schafer claims.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".