Fostering Cultures of Sustainability through Community-Engaged Museums: The History and Re-Emergence of Ecomuseums in Canada and the USA
Bibliographic record
Abstract
In recent decades, communities around the world have been reacting to the forces of globalization by re-focusing on the local, leading to the democratization of culture, heritage, and related concepts. By attempting to reconnect locals with their own sense of belonging, to reinvigorate a pride of place, and to foster wellbeing, communities have increasingly and successfully turned to features that make their local history, heritage, and environment unique or distinctive. In turn, democratization processes have led to sustainable forms of economic and community development through ecomuseums and other examples of community-engaged museums. This paper aims to deepen our understanding of relevant community-based culture and heritage initiatives by reflecting on the development of ecomuseums in Canada and the USA. As part of the larger museum community, ecomuseums tend to be accessible entities that are not affiliated with political or other convictions or viewpoints. This makes them uniquely positioned to foster creative change and adaptation aimed at sustainability, yet their evolution in North America has not been examined from this perspective. To address this gap, this paper will highlight the Haute-Beauce Ecomuseum in Québec and the Ak-Chin Him Dak Ecomuseum in Arizona, which have long histories as North American ecomuseums and represent two very different cultural and geographic contexts. We also reflect on the history of ecomuseums in Canada, and their recent emergence in the Canadian province of Saskatchewan.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".