Bibliographic record
Abstract
With the global outbreak of COVID-19, not only were everyday museum practices affected like never before, but pre-existing problems in the education field were exacerbated. COVID-19 forced children’s museums in particular to take on even bigger educational responsibilities. Through examples from the United States, this paper analyzes how children’s museums came to fulfil their educational potential through their digital programming and related initiatives, thus also amplifying their civic engagement within their local communities. In addition to giving an overview of the available literature, the analysis found that during the pandemic children’s museums supported families of school-aged children and formal educational institutions, which were dealing with the effects of growing digital exclusion. This exclusion was alleviated througha range of digital and non-digital solutions, including virtual experiences and participatory family activities, delivering learning kits, and providing technical or physical infrastructure to school classes. By creating both digital and analogue surrogates for physical exhibitions and education, comparing initiatives and perceived purposes of these programs, the inherent cultural and human value of museums as essential human spaces—not just for objects, but for a high standard of education—can be seen.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".