The impact of COVID-19 on digital data practices in museums and art galleries in the UK and the US
Bibliographic record
Abstract
Abstract The first quarter of 2020 heralded the beginning of an uncertain future for museums and galleries as the COVID-19 pandemic hit and the only means to stay ‘open’ was to turn towards the digital. In this paper, we investigate how the physical closure of museum buildings due to lockdown restrictions caused shockwaves within their digital strategies and changed their data practices potentially for good. We review the impact of COVID-19 on the museum sector, based on literature and desk research, with a focus on the implications for three museums and art galleries in the United Kingdom and the United States, and their mission, objectives, and digital data practices. We then present an analysis of ten qualitative interviews with expert witnesses working in the sector, representing different roles and types of institutions, undertaken between April and October 2020. Our research finds that digital engagement with museum content and practices around data in institutions have changed and that digital methods for organising and accessing collections for both staff and the general public have become more important. We present evidence that strategic preparedness influenced how well institutions were able to transition during closure and that metrics data became pivotal in understanding this novel situation. Increased engagement online changed traditional audience profiles, challenging museums to find ways of accommodating new forms of engagement in order to survive and thrive in the post-pandemic environment.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".