Volunteers’ Roles in Festival Emergency Management: Lessons from Shambhala Music Festival
Bibliographic record
Abstract
This case shares insights from Shambhala Music Festival (SMF), which is an annual, large-scale, independent electronic music event held in British Columbia, Canada. This case examines how SMF, with its 2000+ volunteers, has addressed the challenge of integrating volunteers into formal emergency management systems. By exploring the experiences of Shambhala, this case offers valuable insights into how volunteers contribute to medical services, harm reduction and emergency management to create a safe festival environment. The festival is known for its pioneering on-site services, including medical care, drug checking, and safe spaces for guests. This case is intended for students, professionals and academics in event management, tourism management, emergency planning, and volunteer management. Through strategic partnerships with external organizations, proactive planning, and including volunteers in emergency management structures, Shambhala serves as an example for other events operating in high-risk environments, especially as climate change increases the risk of disaster frequency and severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".