Local Museums, Global Publics: how online programing during COVID-19 impacted the way museums define their audiences
Bibliographic record
Abstract
In the peri-pandemic ‘new normal,’ museums occupy physical and online spaces. Many museums responded to the COVID pandemic by moving much of their programing to online modalities. One consequence of the dramatic increase in online programing compelled by COVID-19 is that previously location-based museum programs are suddenly more accessible to global publics: worldwide populations of cultural heritage stakeholders, defined more by common interest than by geographic location. I hypothesize that increased interaction with global publics during the pandemic has inspired an expansion of museums’ concept of Publics (or key audiences) to include a broader Global Public in addition to their traditional local stakeholders. After the pandemic, moreover, museums will maintain many of the online programs they started in 2020 with the intent to continue engaging Global Publics as part of their patronage. Drawing from a survey of 56 North American Museums and seven ethnographic interviews, representing 18 US States and three Canadian Provinces, this study seeks to contribute to the discourse around the role of museums as cultural heritage institutions amid and following the COVID-19 pandemic. As museums continue to adapt to additional, unprecedented challenges, continual re-evaluation of how the field defines publics will help cultural institutions adapt to fulfill their mandates in an ever-more globalized world.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".