Leveraging Governance Performance to Enhance Climate Resilience
Bibliographic record
Abstract
Abstract Enhancing the resilience of complex social‐ecological systems (SES) to climate change requires transformative changes. Yet, there are knowledge gaps on how best to achieve transformation. In this study, we present an approach for assessing governance performance in SES and identifying leverage points to ultimately enhance climate resilience. The approach combines three different methods including a capital approach framework, fuzzy cognitive mapping, and a leverage points analysis. Using a coastal case‐study in Algoa Bay, South Africa, the performance of governance processes contributing to different forms of capital is assessed. Subsequently, leverage points ‐ where a small shift may lead to transformative changes in the system as a whole ‐ are identified based on measures of centrality and performance. Results suggest that a range of leverage points can improve governance performance and therefore climate resilience in the case‐study. Leverage points include improving (a) support from the provincial government; (b) priority given to climate change in the integrated development plan; (c) frequency of collaborations; (d) participation in the implementation of climate action plans; (e) allocation of funding to climate change actions; (f) the overall level of preparedness in terms of staff with relevant expertise; (g) public awareness and understanding of climate change. The approach can also be used to analyze and model the relations and interactions between capitals. The study advances methodological and theoretical knowledge on the identification of leverage points for enabling transformations toward climate resilience and broader sustainability goals in SES.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".