Public Perceptions of Resilience and Vulnerability Concepts for Adaptation
Bibliographic record
Abstract
Resilience is everywhere in plans, policy, and academic literature on risk reduction and adaptation, and a common refrain of elected officials and disaster victims alike. Geographers have contributed much to the critical understanding of the theoretical foundations and implications of this now ubiquitous concept, and have made some initial steps in studying how local practitioners and other experts interpret and apply resilience in risk reduction and adaptation measures. But there is limited empirical research, however, on what the people living in communities exposed to hazards think about resilience. This study aims to address this gap by conducting in-person, researcher-administered surveys (n = 400) with members of the public using coastal and lakefront environmental amenities in Vancouver and Toronto, Canada. Survey results include three main findings: (1) the majority of participants prefer the framing of “increasing resilience” over “reducing vulnerability”; (2) the conceptualization of resilience as creative transformation is greatly favored over conceptualizations of resilience as resistance or recovery; and (3) resilience is seen as uneven in both study cities. The study reveals insights that can help inform and align resilience theory and practice in cities.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".