live coding: sound – gesture – algorithm
Bibliographic record
Abstract
Text-based musical live coding (Collins et al, 2003) is approached from the notion of gesture as understood in embodied music cognition and sound-based composition such as to propose a framework for sound, movement and algorithms from a combined embodied-epistemic position. Live coding viewed as an extension of multi-scale studio based sound practices (Roads, 2015) for which human listening and machine listening (Collins, 2015; Van Nort, 2013) are the basis for intervention during the development process; yet positioned within the temporal framework of a performance. The programming language is an interface (Blackwell & Aaron, 2015) to a digital instrumental system that is understood as an epistemic tool (Magnusson,2009) that presumes the potential of various forms of machine agency (Brown, 2016 & 2016b; Bown, 2009) and software agents (Whalley, 2009). The necessary formalism(s) of this digital system sets up the conditions for which human compositional and improvisational actions are complimentary: whatever aspects of the code that are not being improvised in the moment are composed/designed, be it by the performer-programmer(s), or by someone or some software prior. The code that is executed is both descriptive and prescriptive as a score (Magnusson, 2011), while presenting itself for further updates . Bricolage programming describes interactive process of writing and executing code, hearing the output, conceptualizing the next move, and so on, as outlined in the process of action and reaction (McLean & Wiggins, 2010). This understanding of live coding presents a distinct approach to the archetypal notion of sound-producing gesture as grounded in embodied music cognition research and developed in sound-based composition.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".