Communities and change in the anthropocene: understanding social-ecological vulnerability and planning adaptations to multiple interacting exposures
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
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.
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 | 0.001 | 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 | 0.001 | 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 it