Understanding vulnerability of coastal communities to climate change related risks
Bibliographic record
Abstract
DOLAN, A.H., and WALKER, I.J., 2003. Understanding vulnerability of coastal communities to climate change related risks. Journal of Coastal Research, SI 39 (Proceedings of the 8th International Coastal Symposium), pg – pg. Itajai, SC – Brazil, ISSN 0749-0208 This paper discusses the concept of vulnerability as characterized in the climate change literature and presents a framework for assessing adaptive capacity. The framework recognizes inherent susceptibilities of humanenvironment systems exposed to climate variability and change. As climate change impacts are unevenly distributed among and within nations, regions, communities and individuals due to differential exposures and vulnerabilities, the framework highlights determinants of adaptive capacity at the local scale and situates them within larger regional, national and international settings. Determinants include: access and distribution of resources, technology, information and wealth; risk perceptions; social capital and community structure; and institutional frameworks that address climate change hazards. This broader approach contrasts typical impact assessments that focus largely on reducing economic detriments of change. The framework provides a methodological starting point that, as a community-based or ‘bottom-up’ approach, yields important insight on local responses to climate change. It also recognizes that short-term exposure to variability is an important source of vulnerability superimposed on long-term change. At the community level, perceptions and experiences with climate extremes can identify inherent characteristics that enable or constrain a community to respond, recover and adapt. As such, local and traditional knowledge is key to climate change research and should be incorporated into research design and implementation. This approach provides locally relevant outcomes that could promote more effective decision-making, planning and management in remote areas susceptible to climate change hazards. As part of a larger study, this approach will be refined with local input to study sea-level rise impacts on one of Canada’s most sensitive coastlines, northeast Graham Island, Haida Gwaii (Queen Charlotte Islands), British Columbia. Preliminary evidence of changes and responses in this area are identified as a brief case study.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".