Case study and analogue methodologies in climate change vulnerability research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it