Layers of Unpredictability: Developing the Aesthetic and Identity of a Network-Based Live Coding Ensemble
Bibliographic record
Abstract
Live coding often involves a feedback loop between machine and human performers who understand the code both through the knowledge of the coding language and the embodied understanding of the output, i.e. the action of listening. The unpredictability of live coding is entangled with and heightened by the considerations behind a remote collective practice and the use of a collaborative interface designed to provide three main channels of communication to the collaborators: written characters, sound, and visuals. The ability to navigate multiple layers of unpredictability, combined with a constant awareness of each performer’s individual aesthetic and the accumulation of individual and group experiences are the foundations for an ensemble to develop a group identity. In this paper, we’ll discuss each of these foundational block s through the lens of the authors’ experiences in building such a collective aesthetic within the Supercontinent ensemble.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".