Developing Software to Promote Creative Play in Music Education
Bibliographic record
Abstract
This presentation demonstrates novel approaches under development using software to encourage methods of musical study that prioritize playful interaction with music as sound. Originating in earlier projects that developed such software for use in music analysis research, the current project, Digital Playgrounds for Music (DPfM), is working to adapt and extend this approach to facilitate its use in a broad range of educational contexts such as in primary and secondary schools, and undergraduate courses, as well as in wider cultural contexts. The presentation will demonstrate the software in use and show interactive materials illustrating its potential in a variety of situations. The underlying philosophy of an approach that foregrounds sound and playful interaction in music education will be discussed. This facilitates broadening of the curriculum to incorporate often neglected repertoire and for which traditional western musical terminology and notational practice is not best suited (e.g. oral/aural traditions, improvised music, much computer music and avant-garde music). It also makes music pedagogy more accessible to those students who, at least initially, lack the traditional music literacy skills by focusing on sound rather than written text.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".