Bibliographic record
Abstract
Xenakis often uses complex superimposed polyrhythms that are uniquely challenging for performers to realize, especially in solo and chamber works. Faced with overwhelming rhythmic complexity, performers must choose a strategy to navigate the score, often resulting in informal compromises and approximations. As an alternative, in this chapter, I propose a methodological approach to performance of rhythmic complexity using re-notation informed by theories of metric perception. Using examples from Mists, À l’île de Gorée, and Dikhthas, I identify three classes of rhythmic textures: irregular a-metrical rhythms, isochronous polyrhythms, and phase-shifted polyrhythms. While Xenakis’s notation clearly conveys these distinct ideas, it does not facilitate accurate realization in performance, which diminishes the polyphonic quality of these passages. My methodology proceeds in several stages: a) Analysis of the primary cues for grouping and possible metric interpretations of each rhythmic texture using the theory proposed by London; b) using the notion of context-sensitive constraints on metric perception (I propose a quantization strategy to reduce the complexity of the rhythm while preserving the macro characteristics; c) proposal of several possible re-notations, depending on the grouping cues or pulse layers that are taken as the tactus in the passage, while relegating other layers to ana-metrical status. Since multiple versions may be beneficial in representing different aspect of the music, I consider their advantages from both performers’ and listeners’ perspectives; d) finally, I examine the dynamics of listening and synchronization between multiple players in realizing rhythmically complex passages, in which players create a metric interpretation collaboratively.
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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 | 0.001 | 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 | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".