Social vulnerability in three high-poverty climate change hot spots: What does the climate change literature tell us?
Bibliographic record
Abstract
Abstract This paper reviews the state of knowledge on social vulnerability to climate change in three hot spots (deltas, semi-arid regions and snowpack- or glacier-fed river basins) in Africa, Central Asia and South Asia, using elements of systematic review methods. Social vulnerability is defined as a dynamic state of societies comprising exposure, sensitivity and adaptive capacity. We examine whether the hot spots have specific characteristics that tend to increase or decrease social vulnerability, consider suitable scales of analysis for understanding vulnerability, and explore the conceptions of vulnerability adopted in the climate change literature and the nature of the insights this generates. Finally, we identify knowledge gaps in this literature. All three hot spots are characterized by high levels of natural resource dependence, with increasing environmental degradation. They also exhibit unequal policies and patterns of development, which benefit certain segments of society while making others more vulnerable. Vulnerability is driven by multiple factors operating at different scales; however, characterization of cross-scalar interactions is poorly developed in the majority of studies reviewed. Most studies are either large scale, such as broad comparisons of vulnerability across countries, or local, documenting community-level processes. Detailed understanding of the interactions between climate change impacts on natural systems, and socio-economic trajectories, including adaptation, also emerges as a knowledge gap.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".