Music education and community development in Vancouver's Downtown Eastside : an ethnographic case study of the Carnegie Centre Jazz Band
Bibliographic record
Abstract
This ethnographic case study focuses on the Carnegie Centre Jazz Band, a city-run community music program for adult learners at the Carnegie Centre, a community centre in the heart of Vancouver’s most aggrieved and marginalized area, the Downtown Eastside (DTES). I have been playing with the Carnegie Jazz Band, a free and open program at the Centre, since January 2010. As a participant-observer between February and April 2013, I conducted three private audio-recorded interviews with twelve of the fifteen regular members of the band who consented to participate, including Brad Muirhead, the bandleader. They provided information on their reasons for joining the band, why they continue to participate, what they gain from the experience, and what they hope for as outcomes of their participation. In this thesis, I examine the benefits that music making, specifically jazz and creative improvisation, provide for the band members, showing how they see themselves as music-makers within the program, identifying the challenges they face in participating, and situating their involvement in the larger paradigms of community music and communities of practice. The factors that motivate the individual members of the band to participate are myriad, but they all share an interest in and a commitment to supporting one another’s learning. One of the main findings of this case study is that approaching music-making with an aesthetic and ethos of improvisation is central to the band’s success in the aggrieved and marginalized DTES.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".