The Application of Resilience Assessment: Resilience of What, to What, with What? A Case Study Based of Caledon, Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
"Resilience assessment can be used to determine major issues, stakeholders, vulnerabilities, and opportunities of a social–ecological system to enhance resilience. A resilience assessment was conducted on the Town of Caledon, Ontario between 2010 and 2011 using the Resilience Assessment Workbook for Practitioners (version 1). The assessment explores the following three questions: Resilience of what? Resilience to what? Resilience with what? The answer to the first question describes the history, main issues, and stakeholders of the focal system. The answers to the remaining two questions describe potential resilience threats and assets, respectively. The assessment results include (1) identified resilience threats and assets of Caledon as a social–ecological system in the context of urban growth; (2) a cross-scalar study of Caledon in its ecological, social, and economic domains; (3) interviews with 26 community members on the topics of urban growth and resilience; and (4) recommendations for Caledon to enhance its resilience in face of urban growth pressures. The results reveal the significance of continual learning, engaged citizenship, and cross-scalar collaboration between governmental bodies. The assessment results also highlight some particular features that would enhance the resilience of Caledon, such as nurturing the health of agroecosystems, developing trade-off rules for conflict resolution, and treating low-impact urban development as an opportunity. This research provides a case study of resilience assessment of a community that undergoes a rural–urban divide. Emerging themes of resilience are identified. Research limitations and suggestions are presented at the end of this paper."
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it