<i>Audible Ecosystemics</i>as Artefactual Assemblages: Thoughts on Making and Knowing Prompted by Practical Investigation of Di Scipio's Work
Bibliographic record
Abstract
By exploring Di Scipio's Audible Ecosystemics through the optic of a succession of practical student projects we see that the processes and forces involved in the making can in turn be viewed as an ecosystem. Some key aspects of this revolve around the ways in which technical and social matters interweave in practice—such as negotiating transitions between coding and practising—and how musical identities and design choices can interact. I draw from this the thought that the dynamics of the negotiation between the technical and social are a key aspect of electronic musical craft, but that this topic remains sparsely accounted for in our discourse. I suggest that devising better means of articulating about such negotiations—and about practice more generally—is a way in which practice-led research in this area can contribute usefully to the wider endeavour of musical research.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.057 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".