Pandemic Preparedness in the Live Performing Arts: Lessons to Learn from COVID-19 in the G7 Countries: Project Report
Bibliographic record
Abstract
This report publishes the findings of the British Academy-funded Pandemic Preparedness: Lessons to Learn from Covid-19 across the G7 project. Between April 2023 - January 2024, a UK-led research team with Co-Investigators in the USA, Canada and Germany and Research Associates in France, Italy and Japan examined the lessons learned from the responses of the live performing arts sector and governments to COVID-19 in the G7 countries. We focused our attention on policy interventions by governments and funders alongside the individual responses by workers in the live performing arts as well as organisations and their audiences. We further considered the impact of the pandemic on digital modes of working and disseminating creative content; how the pandemic affected communities, places and how ‘cultural value’ is understood; and what the pandemic revealed about systems and structures in the sector. The aim was to support sector preparedness for future crises, whether caused by new pandemics, climate-related disasters, demographic changes, economic pressures or the impacts on the live performing arts of national and international politics. This full report consists of detailed literature reviews of how the pandemic affected the performing arts sector in the United Kingdom, the USA, Canada and Germany; it also contains shorter literature reviews which focus on France, Italy and Japan. This research underpins the policy recommendations which are published in separate reports.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".