Organizational Responses to COVID-19 in Canadian Symphony Orchestras - Six Case Studies
Bibliographic record
Abstract
This study is a collection of six case studies of Canadian orchestras which examines the differences in organizational responses to the COVID-19 pandemic. Case studies method is utilized to explore the connection between the various facets of the external environment and the orchestras. The research question “How do Canadian symphony orchestras monitor and respond to their organizational environment in relation to COVID-19?” frames the focus of the study to understand how various environmental and organizational contingencies affect the organizational actions in response to the crisis caused by the pandemic. Each case study is composed of a research report with a comprehensive description of the orchestra and its environment, a discussion of the impact of the COVID-19 pandemic, and a detailed account of how each orchestra has responded to the pandemic crisis. Five hypotheses are analyzed and discussed using a theoretical framework. The framework merges the basic concepts of resource dependency theory and population ecology theory and incorporates four different units of analysis. The results of the research revealed each case orchestra had unique traits, contingencies and the external environment that influenced the organizational actions they took to mediate the impact of the pandemic. In general, the large and more professional orchestras with more than 10 million dollars in annual revenue, focused on keeping their musicians working, maintaining their organizational presence in the community and increasing legitimacy through various partnerships. Medium sized orchestras with annual revenue between 1 to 10 million dollars, have focused on keeping their concerts and the existing audience base. Small and/or community-based orchestras with under a million in their annual revenue focused on keeping the organizational structure and reaching out to their existing donor/audience base. The pandemic has forced all six case orchestras to act fast and move most of their organizational functions online and make the online presence more permanent.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".