Getting Out of the Black Box: analogising the use of computers in electronic music and sound art
Bibliographic record
Abstract
The process of creating computer-based music is increasingly being conceived in terms of complex chains of mediations involving composer/performer and computer software interactions that prompt us to reconsider notions of materiality within the context of digital cultures. Recent scholarship has offered particularly useful re-evaluations of computer music software in relation to musical instrumentality. In this article, we contend that given the ubiquitous presence of computer units within contemporary musical practices, it is not simply music software that needs to be reframed as musical instruments, but rather the diverse material strata of machines identified as computers that need to be thought of as instruments within music environments. Specifically, we argue that computers, regardless of their technical specifications, are not only ‘black boxes’ or ‘meta-tools’ that serve to control music software, but are also material objects that are increasingly being used in a wide range of musical and sound art practices according to an ‘analog’ rather than ‘digital’ logic. Through a series of examples implicating both soft and hard dimensions of what constitutes computers, we provide a preliminary survey of practices calling for the need to rethink the conceptual divide between analog and digital forms of creativity and aesthetics.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.037 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".