Bibliographic record
Abstract
In the context of escalating climate change impacts, the assessment and quantification of resilience emerge as indispensable tools for managing risks and bolstering adaptive capacity across diverse scales. This chapter elucidates the pivotal role of resilience calculation amid the backdrop of climate change effects. It navigates through a spectrum of methodologies, models, and frameworks utilized for resilience assessment, while also addressing the ramifications of climate change on resilience enhancement endeavors. By delving into conceptual underpinnings, such as the social-ecological systems framework and the adaptive cycle model, as well as practical applications like network analysis and scenario planning, this chapter provides a comprehensive exploration of resilience calculation. Additionally, it examines real-world case studies to illuminate key insights and best practices in resilience assessment and planning. Through this discourse, stakeholders gain valuable perspectives on leveraging resilience metrics, incorporating climate change effects, navigating challenges, and charting future directions in resilience research and policy.
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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".