Adaptive capacity: from assessment to action in coastal social-ecological systems
Bibliographic record
Abstract
Because of the complexity and speed of environmental, climatic, and socio-political change in coastal marine social-ecological systems, there is significant academic and applied interest in assessing and fostering the adaptive capacity of coastal communities. Adaptive capacity refers to the latent ability of a system to respond proactively and positively to stressors or opportunities. A variety of qualitative, quantitative, and participatory approaches have been developed and applied to understand and assess adaptive capacity, each with different benefits, drawbacks, insights, and implications. Drawing on case studies of coastal communities from around the globe, we describe and compare 11 approaches that are often used to study adaptive capacity of social and ecological systems in the face of social, environmental, and climatic change. We synthesize lessons from a series of case studies to present important considerations to frame research and to choose an assessment approach, key challenges to analyze adaptive capacity in linked social-ecological systems, and good practices to link results to action to foster adaptive capacity. We suggest that more attention be given to integrated social-ecological assessments and that greater effort be placed on evaluation and monitoring of adaptive capacity over time and across scales. Overall, although sustainability science holds a promise of providing solutions to real world problems, we found that too few assessments seem to lead to tangible outcomes or actions to foster adaptive capacity in social-ecological systems.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".