Community-based music making and music-based community making: The case of the We Are All Musicians (<scp>waam</scp>) project
Bibliographic record
Abstract
In this essay, percussionist and community arts worker Jesse Stewart discusses his work in the field of participatory creative music through We Are All Musicians ( waam ), an organization and ongoing research-creation project he started in 2012. waam is dedicated to making music as broadly accessible as possible, particularly among communities that have experienced barriers to making music, notably children and adults living in low-income situations, individuals with disabilities of various kinds, and the elderly. Stewart discusses several participatory creative projects that have resulted from partnerships between waam and community organizations including Regina Street Alternative School (formerly Regina Street Public School) in Ottawa; Being Studio (formerly H’Art of Ottawa); the Alzheimer’s Society of Ottawa, Artswell, the Bruyère Continuing Care Centre in Ottawa, and the National Arts Centre. He goes on to discuss several interactive music/sound installations that he has set up in public spaces to facilitate interactive improvisatory musical interaction across various forms of difference. The essay concludes with a discussion of some waam initiatives that took place online during the covid-19 pandemic, using networked improvisation to facilitate musical interaction and community formation across both difference and distance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.069 | 0.060 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".