Communities and change in the anthropocene: understanding social-ecological vulnerability and planning adaptations to multiple interacting exposures
Bibliographic record
Abstract
The majority of vulnerability and adaptation scholarship, policies and programs focus exclusively on climate change or global environmental change. Yet, individuals, communities and sectors experience a broad array of multi-scalar and multi-temporal, social, political, economic and environmental changes to which they are vulnerable and must adapt. While extensive theoretical—and increasingly empirical—work suggests the need to explore multiple exposures, a clear conceptual framework which would facilitate analysis of vulnerability and adaptation to multiple interacting socioeconomic and biophysical changes is lacking. This review and synthesis paper aims to fill this gap through presenting a conceptual framework for integrating multiple exposures into vulnerability analysis and adaptation planning. To support applications of the framework and facilitate assessments and comparative analyses of community vulnerability, we develop a comprehensive typology of drivers and exposures experienced by coastal communities. Our results reveal essential elements of a pragmatic approach for local-scale vulnerability analysis and for planning appropriate adaptations within the context of multiple interacting exposures. We also identify methodologies for characterizing exposures and impacts, exploring interactions and identifying and prioritizing responses. This review focuses on coastal communities; however, we believe the framework, typology and approach will be useful for understanding vulnerability and planning adaptation to multiple exposures in various social-ecological contexts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".