Musical performance opportunities in Edmonton’s community leagues: Mapping a community music ecology
Bibliographic record
Abstract
As a community musician seeking performance opportunities in Edmonton, I undertook a systematic exploration of the city’s 162 community leagues to understand their musical potential. What began as a practical curiosity about finding venues evolved into discovering an extensive but largely hidden musical ecosystem embedded within neighbourhood infrastructure. My environmental scanning revealed surprising results: 108 leagues demonstrated clear capacity for musical programming, yet most musical activity operated below the radar, woven into community events like volunteer appreciation dinners, seasonal celebrations and fundraising gatherings rather than existing as formal cultural programming. Music functioned as an enhancement to community life rather than as isolated entertainment. This discovery led me to develop a ‘performance ecology’ framework – a way of understanding how musical opportunities exist within interconnected networks of institutions, musicians and community members. Community music scholars helped me recognize that this ecological approach aligned with broader theoretical frameworks, particularly cultural democracy principles and social capital development, demonstrating how musical activity builds both bonding capital within neighbourhoods and bridging capital across different community groups. The systematic methodology I developed is transferable to other communities regardless of their specific infrastructure. By documenting over thirty-five types of community events with musical potential and identifying patterns across demographic groups and ongoing programmes, I found that most communities already possess both musicians and venues – what is often missing are the connections between them. This practitioner-led exploration offers concrete strategies for musicians, community organizations and cultural advocates seeking to enhance musical engagement. The key insight: community music is not something you add to community life – it is something you discover within it and help flourish through attention, connection and strategic support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".