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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.017 |
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; both teacher heads agree on what is shown here.
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".