9. Iannis Xenakis’s Philosophy of Music, Stochastics, and the Postmodern Sublime
Bibliographic record
Abstract
In his writings and compositions from the mid-1950s to the early 1960s, Iannis Xenakis makes clear his intent to compose music that escapes the inherited structures of the Western tradition. As a way to achieve this goal, he uses stochastic techniques in order to uproot listeners’ expectations, but in a manner more attuned to the psychology of listening than his serialist contemporaries. One of his chief concerns is the hierarchy of temporalities as experienced by the listener and manipulated by the composer, a hierarchy whose levels range from the length of an entire piece down to microscopic sonic ‘grains’. His usage of stochastic techniques muddies the distinction between these temporal levels, disorienting the listener on multiple timescales. Such a state of disorientation, where one is unable to predict the course of a work, is a hallmark of what Jean-François Lyotard calls the ‘postmodern’ sublime, which is produced by avant-garde artistic works that, like those of Xenakis, eschew received models and notions of representation or allusion. Drawing on scholarship by Brian Kane, I argue that Lyotard’s postmodern sublime is precisely what Xenakis aims to produce in his stochastic works, and that it acts as a preliminary mode of both composition and reception, helping composer and listener to break free from traditional constraints. This process allows for the production of new compositional structures and modes of reception, radically expanding musical possibilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".