Approaches to notation for embodied engagement with a novel neural network-based musical instrument
Why this work is in the frame
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Bibliographic record
Abstract
Introduction The growing complexity and breadth of sonic possibilities enabled by sound synthesis technologies give rise to significant challenges for the notation of music, especially in light of emerging neural network-based paradigms. Prescriptive vs. descriptive notation has emerged as a paradigm with relevance to this challenge. Methods Experienced musicians ( n = 11) were asked to compose for a novel neural network-based digital musical instrument and were prompted to produce descriptive and prescriptive graphic notations. Results Group differences in task conceptualisation were observed, while further analysis revealed perpendicular dimensions of variation in the resulting musical notation, which were associated with perceived creativity support. Discussion Based on this analysis, a conceptual framework is proposed that suggests useful strategies for music composition and creative endeavours. Summary Beyond the representation of sound and/or action, use of abstraction and metaphor emerge as strategies for music notation, with consequences for support of creative work.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it