Phenomenological open graphic notation with chaotic systems in interactive electroacoustic music
Bibliographic record
Abstract
Abstract This article explores phenomenological open graphic notation as an effective scoring method for instrumentalists engaging with chaotic systems in interactive electroacoustic music. Open graphic notation has long provided composers with a means of fostering interpretative freedom in musical performance. The subjective nature of open graphic scores establishes a dynamic relationship between the score and the performer that parallels the interactions between musicians and chaotic systems in interactive electroacoustic music. Chaotic systems, characterised by their non-linear and unpredictable behaviour, often necessitate improvisatory approaches rather than reliance on fixed notation. However, notation can serve as a structural framework, affording composers greater formal control while supporting performers who may be less accustomed to improvisation. How, then, might notation be used with chaotic systems in interactive electroacoustic music? Drawing on phenomenological concepts such as the lived body, embodied action and Gestalt perception, this notational approach can provide a structured yet flexible means of guiding performer–system interactions. The author presents three recent compositions as case studies, demonstrating how phenomenological open graphic notation can shape and mediate the performer’s engagement with chaotic systems in interactive electroacoustic music.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".