Science museums in a time of crisis: Insights from Brazil, Canada, Finland and Portugal
Bibliographic record
Abstract
This research examines how science museum teams in different countries addressed crises during the COVID-19 pandemic, and what values, priorities and practices emerged during this challenging time. Informed by theoretical perspectives related to broken world thinking and care, this paper summarizes and discusses findings related to four qualitative case studies: the Museum of Tomorrow (Brazil), the Royal BC Museum (Canada), Heureka the Finnish Science Centre (Finland) and the National Museum of Natural History and Science (Portugal). Data gathered during 2023–2024 centred on the voices and lived experiences of 26 museum professionals, as well as documents and artefacts shared by them. Findings highlight how local realities shaped institutional responses to the pandemic, and how all four institutions nurtured caring relationships, faced uncertainty through reflective attentiveness and became relevant civic and scientific resources in a time of crisis. The research shed light on how care can become a central pathway to guide the transformation of science museums both inwardly and outwardly.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".