Expanding the music circle through networked improvisation in an inclusive ensemble
Bibliographic record
Abstract
People with disabilities are confronted with many barriers to participation in inclusive music making, including but not limited to challenges accessing appropriately adapted program curricula and pedagogical approaches. This article reports on a partnered research project ‘Expanding the Music Circle’ that brought professional orchestra musicians, special music educators and adults with profound disabilities together to make improvised music online via Zoom. The authors, experts in improvisation pedagogy and special music education, designed and delivered a curriculum aimed at facilitating an inclusive ensemble experience for all participants. Following a modified Participatory Action Research (PAR) methodology, the study comprised 16 facilitated improvisation sessions for adults with disabilities, with observation and feedback by orchestra musicians and special music educators. Subsequently, the three participant groups were formed into integrated ensembles for eight additional sessions. Data, analysed through thematic coding, includes participant observation, videoed Zoom calls, journals, focus groups and interviews. Results include positive feelings of community in the integrated ensemble, mixed experiences making music using networked, online technology and the value of using accessible and adaptable improvisation in a mixed abilities ensemble, especially when presented with some predictability and structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".