Data- and interaction-driven approaches for sustained musical practices with machine learning
Bibliographic record
Abstract
While contemporary discourse on AI often fixates on large ‘foundation’ models, these types of models often offer only limited ability for musicians to express their musical intentions and agency. In this article, following a brief reflection on and critique of the musicality of large music models informed by our own experimentation, we describe a richer set of possibilities for integrating machine learning into musical practices. We outline several alternative approaches within machine learning which can better support sustained musical practices. These include approaches underpinned by an understanding of training data as a vehicle for communicating intention (as opposed to a representation of ‘ground truth’), approaches that leverage small datasets and models, and approaches that employ other interactive mechanisms to capture human intention and agency. We present examples of our own musical projects that illustrate these approaches, and we discuss the implications of our alternative perspectives on data and intention for system development, musical practice, future research, and the future of musicking.
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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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".