Bibliographic record
Abstract
Abstract The onset of COVID-19 has altered the functioning of museums and biennials across the world. Consistent with the ‘deep dive approach’ employed in this book, the chapter takes a close look at three museums and a biennial. The museums chosen for detailed examination are ‘The Guggenheim’ (USA), the Victoria Memorial Hall (India), and the Montreal Museum of Fine Arts (Canada). The biennial chosen for detailed exploration is the ‘Kochi-Muziries Biennale’ (India). The objectives of the exercise are to understand the nature of operations carried out by these organizations and identifying the economic and managerial challenges faced by them prior to and during the difficult days of COVID-19. The chapter explores how the four organizations have coped with the pandemic. While Guggenheim opened up their premises on a minimal note and employed intense virtual media exposes, the Kochi-Muziries Biennale was constrained to postpone its biennial event. The Montreal Museum and the Victoria Memorial Hall also underwent protracted bouts of inactivity. The dive deep studies throw up a menu of management-related situations that holds vital lessons for budding museum managers. Similarly, the functioning of the four organizations also afford valuable lessons to the policy establishment regarding the effectiveness of cultural and arts policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.005 |
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".