The Great Sustainability Challenge: How Visitor Studies Can Save Cultural Institutions in the 21st Century
Bibliographic record
Abstract
ABSTRACT Sustainability will be the great challenge for many cultural institutions in the first quarter of the 21st Century. Changing patterns of government, corporate, and individual support, new demographics, and new ways of creating, preserving, and sharing information are all challenging the sustainability of museums and other cultural institutions. In 2001 a meeting of science museum leaders in Bristol identified 3 distinct but interacting dimensions to sustainability: financial, intellectual, and social. Financial crises are the most obvious threats to the survival of institutions, but intellectual and social weaknesses can be equally dangerous and can make a temporary financial problem fatal. Visitor studies have the potential to provide crucial understandings that cultural institutions will need to build new, more sustainable models than the ones which served the previous century. ACKNOWLEDGMENTS I thank Carol Enseki, Ross Loomis, Karine Lepeuple, Mary Ellen Munley, Cary Tisdale, and all of my fellow participants from the Bristol University meeting for their ideas, comments, and conversations which have helped shape this article. Notes 1After this article was written Mary Ellen Munley pointed out to me that Mark Moore's analysis of sustainability for government agencies (CitationMoore, 1995) also identified three dimensions of sustainability. Comparing Moore's analysis with the one here is instructive and demonstrates significant variances between critical issues for cultural institutions and those for government agencies. Moore's dimensions are different from the ones in this article in part because most government functions have complex, overlapping command structures, in contrast to the simple hierarchical structure of museums and most cultural organizations. In addition, government agencies have single or narrowly limited possible sources of funds, whereas cultural organizations typically have a broad multiplicity of funding sources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".