Measuring Rural Well-being toFoster Collaboration: A Meta-analysis of Sustainability Indicator-basedMonitoring Initiativesin Rural and Resource-based Regions
Bibliographic record
Abstract
The proposed presentation will discuss the potential for sustainability indicators to support more participatory governance in rural and resource-based regions. Sustainability indicators (SIs) are a widely-used tool for communities and regions to identify locally-defined measures of well-being and monitor their progress over time. However, their use in rural communities is limited, with most SI frameworks originating from urban areas or international tools. One positive outcome of many SI initiatives, including those in rural and resource-based regions, is the initiation of dialogue among diverse stakeholders in communities where indicators are developed, often leading to improved relationships, trust, and social learning. In this way, SIs have much in common with the concept of collaborative governance, an approach to shared decision-making that is well suited for supporting regional-scale rural development based on collaboration between different communities, sectors, and levels of government. The primary focus of the presentation will be on discussing the results of a meta-analysis that examines SI initiatives in rural communities and regions of North America. This meta-analysis includes existing examples of local monitoring initiatives that have used indicators of sustainability in rural and resource-based regions. The presentation will discuss the analytical framework that was developed to examine these initiatives, which draws from literature on governance, social learning, and sustainability monitoring. Preliminary findings of the meta-analysis will also be shared, with an emphasis on initiatives that were effectively integrated into local governance processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".