Exploring the modifying effects of adaptive capacity on resilience to climate change across 4 coastal cities in British Columbia, Canada
Bibliographic record
Abstract
Coastal communities are particularly at risk from the intensifying impacts of climate change and must act quickly to implement adaptation measures to enhance resilience. Adaptive capacity is recognized to have a modifying effect on resilience. Understanding the combination of factors that affect adaptive capacity is essential for prioritizing actions necessary to respond to existing and predicted impacts, and thus reduce vulnerability. In this study, we draw on the experience of four coastal cities in southwest British Columbia, Canada, to understand how adaptive capacity contributes to resilience. Specifically, through qualitative key actor interviews, we investigate how the different domains of adaptive capacity strengthen and constrain resilience in practice. Findings reveal that the domains of adaptive capacity are closely interconnected. For instance, effective organization and ongoing learning have strengthened the agency of local government decision-makers to act; however, inflexibility in institutional responses to climate change and a lack of necessary assets appears to constrain efforts. Our results indicate that leveraging the organization and learning domains of adaptive capacity through increased education and improved intergovernmental collaboration may bolster resilience. • Efforts to strengthen local government adaptive capacity must be ramped up • The domains of adaptive capacity exhibit strong interdependence • To build resilience, attention to the interconnected nature of the domains is critical • Enhancing collaboration can address jurisdictional barriers and increase flexibility • Bolstering climate knowledge can improve resource dedication and proactive planning
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".