Environment, intention and intergenerational music making: Facilitating participatory music making in diverse contexts of community music
Bibliographic record
Abstract
Abstract Current conversations and debates amongst community music and music educational practitioners have engendered the need to identify and describe qualities and leadership strategies that could be expected essential for those in teaching, facilitating and/or working in diverse settings, including carceral environments. Common areas are first explored: where are we working (context)?, with whom are we working (people/community)? and given an understanding of the first two questions, how do we do it (strategies)? These framing questions assist in locating common characteristics of making music in various settings, but also point to the distinctive features of each of the three contexts. By establishing conditions for authentic experience, safety in exploring and risk-taking as well as defining key strategies for successful engagement, instructional approaches are identified and applied. Pedagogical practices that include instructional strategies such as guided discovery, collaborative learning and narrative dialogue are identified. Facilitation processes such as, for example, demonstrating/modelling, coaching, Socratic direction and facilitating/enabling are models of musical intervention that create space for acquiring and using lifelong skills in participatory contexts. Whether in schools, communities or prisons, the positive experience of music making thrives where the flexibility of the teacher/facilitator, the reflexivity of the innovator, the foundational knowledge that research and practice provide and the ultimate enhancement of the community are fully in place.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".