Case study and analogue methodologies in climate change vulnerability research
Bibliographic record
Abstract
Abstract Assessing vulnerability is an important component of human dimensions of climate change (HDCC) research. Vulnerability assessments identify and characterize who and what are sensitive to climatic risks and why, characterize adaptive capacity and its determinants, and identify opportunities for adaptation. This paper examines the importance of case study and analogue methodologies in vulnerability research, reviews the historical evolution of the two methodologies in the HDCC field, and identifies ways in which they can be used to increase our understanding of vulnerability. Case studies involve in‐depth place‐based research that focuses on a particular exposure unit (e.g., community, industry, etc.) to characterize vulnerability and its determinants. Temporal analogues use past and present experiences and responses to climatic variability, change and extremes to provide insights for vulnerability to climate change; spatial analogues involve conducting research in one region and identifying parallels to how another region might be affected by climate change. Vulnerability research that uses case studies and analogues can help to develop an understanding of the determinants of vulnerability and how they interact, and identify opportunities to reduce vulnerability and enhance adaptive capacity to current and future climate risks. This information can assist policy makers in developing adaptation plans and to mainstream climate change adaptation into other policy‐ and decision‐making processes. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
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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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".