How Ecosystem-Based Adaptation to Climate Change Can Help Coastal Communities through a Participatory Approach
Bibliographic record
Abstract
Coastal rural communities worldwide face many challenges not only related to climate change but also extreme events, environmental degradation, population growth or aging, and conflict usage of the ecosystem. Historically, the economies of coastal communities have been based on the exploitation of natural resources, thus shaping its socioeconomic development. This has led to some limitations in the way these communities can now adapt to climate change. In Canada, coastal communities are increasingly dealing with climate change consequences. Sea level rise, coastal erosion, and increasing frequency in storm surges threaten the fragility of both natural and human systems. Various approaches have been used to try to reduce the vulnerability and improve adaptive capacity of communities. One approach, promoted by many organizations including the United Nations, is ecosystem-based adaptation. This approach is part of the series of nature-based solutions that help social–ecological systems become more resilient; by promoting biodiversity conservation and ecosystem services, this approach also relates to principles of community engagement and supports adaptive governance and social inclusion. This paper describes and analyzes these principles and considers strategies for ensuring community engagement. Combining ecosystem-based adaptation with a strong community engagement can enhance the long-term sustainability of the social-ecological system.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".