Toward an aural aesthetics of 24/7 environments: Beethoven, audio stretching, and techno-indeterminacy
Bibliographic record
Abstract
This essay offers a critical analysis of Leif Inge’s sound installation 9 Beet Stretch, exploring the piece’s complex relations both to its musical “source” (a recording of Beethoven’s 9th Symphony) and to the technical process (audio stretching) that sustains it. The Stretch effectively allows us to listen to Beethoven’s 9th for a duration of 24 hours without distorting the pitch or other sonic qualities of the original recording. The result is an acoustically impossible experience that brings us uncannily close to Beethoven’s masterpiece in its structure and sonic materiality, while simultaneously pushing Beethoven into the background of a diffuse sonic environment in which our own embodiment and experience of listening come to the fore. I propose the term “techno-indeterminacy” (based on John Cage’s notion of indeterminacy in composition) to describe the imbrication of musicological, aesthetic, and material registers that Inge’s piece both celebrates and suspends by means of a technical process. Moving critically from Cage’s indeterminacy to Mark B. N. Hansen’s theory of affective embodiment, I argue that the sonic environment of Inge’s 24-hour installation ultimately merges with the totalizing 24/7 environment of digital capitalism as recently sketched out by Jonathan Crary—and prefigured philosophically in Adorno’s writings on modern music. Techno-indeterminacy characterizes not only the aural aesthetics of Inge’s piece, but also our lived experience in the total technological environment of digital capitalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".