Museums in the Pandemic
Bibliographic record
Abstract
Throughout human history, the spread of disease has closed borders, restricted civic movement, and fueled fear of the unknown; yet at the same time, it has helped build cultural resilience. On 11 March 2020 the World Health Organization (WHO) classified COVID-19 as a pandemic. The novel zoonotic disease, first reported to the WHO in December 2019, was no longer restricted to Wuhan or to China, as the highly contagious coronavirus had spread to more than 60 countries. The public health message to citizens everywhere was to save lives by staying home; the economic fallout stemming from this sudden rupture of services and the impact on people’s well-being was mindboggling. Around the globe museums, galleries, and popular world heritage sites closed (Associated Press 2020). The Smithsonian Magazine reported that all 19 institutes, including the National Zoo and the National Museum of the American Indian (NMAI), would be closed to the public on 14 March (Daher 2020). On the same day, New Zealand’s borders closed, and the tourism industry, so reliant on international visitors, choked. Museums previously deemed safe havens of society and culture became petri dishes to avoid; local museums first removed toys from their cafés and children’s spaces, then the museum doors closed and staff worked from home. In some cases, front-of-the-house staff were redeployed to support back-of-the-house staff with cataloguing and digitization projects. You could smell fear everywhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.086 | 0.009 |
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".