Drivers for Resilience in Cultural Organizations: lessons from the Montreal festivals in the face of the COVID-19 pandemic
Bibliographic record
Abstract
SHORT PAPER. Crises such as the COVID-19 pandemic represent appropriate moments to innovate. Many organizations in the cultural sector have thus proposed numerous changes in their activities trying to develop new forms of symbiosis, bringing back the notion of resilience. Beyond its buzz word aspect, resilience has essentially been associated with a set of organizational capacities to adapt and innovate in the face of a disruption in the cultural environment, leaving little consideration to question the main drivers of resilience in cultural organizations. We propose then to study the adaptation of the Montreal festivals offer, building on primary data from 8 interviews with festival directors or managers and secondary data from internal and external documentation. We therefore mobilize the concept of the business model to identify and discuss the drivers for resilience in cultural organizations. We show a trend for festivals to come back to their formal business model despite the deployment of different innovations and identify role and purpose as the two main drivers for the resilience of festivals. Finally, we call for a comparison with other cultural organizations to discuss the preserving and reconfiguring aspects of their resilience.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".