Rural Community Engagement for Heritage Conservation and Adaptive Renewal
Bibliographic record
Abstract
Systems thinking can shed light on important relationships and conditions that affect community engagement activities. While robust tools like the community capitals framework and the sustainable livelihoods approach provide valuable context for engagement projects, additional insights can stem from models that describe the ebb and flow of different types of capital. This paper uses a well-studied ecosystem model called adaptive renewal (AR) to contextualize heritage-related challenges and opportunities in four rural communities on the Canadian prairies. Based on a reflective case-study analysis, we applied the AR model to focus group and semistructured interview data collected as part of a Museums Association of Saskatchewan (MAS) project aimed at using local heritage assets to build sociocultural and environmental capacity and attract investment. The MAS project identified four themes that could be addressed through training and policy changes, including concerns about funding, limited human resources, a lack of public services, and a desire to preserve and build on memories. By mapping each community onto the AR model, we uncovered additional insights about community resilience and other heritage-related challenges and opportunities. The AR model is likely to be a valuable tool for planning or assessing community engagement projects because it reflects the dynamic nature of socioeconomic and cultural relationships that affect community dynamics and local well-being.
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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 | 0.009 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".