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Record W4413382185 · doi:10.1080/20551940.2025.2539009

“The spaces of sight and sound” – Containment, feedback, and contingency in R. Murray Schafer´s acoustic ecology

2025· article· en· W4413382185 on OpenAlexaboutno aff
Maren Haffke

Bibliographic record

VenueSound Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicCybernetics and Technology in Society
Canadian institutionsnot available
Fundersnot available
KeywordsContingencySound (geography)SightEcologyContainment (computer programming)Human echolocationGeographyAcousticsComputer scienceBiologyLinguisticsPhysicsPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.272
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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