Fostering resilience in coastal communities in the context of climate change
Bibliographic record
Abstract
Global coastal communities are facing uncertainty and change from a number of different sources including economic challenges, changing demographics or public policy negligence. A changing global climate adds additional complexity. The tourism and fisheries sectors, often sources of employment in coastal communities, are facing changing natural systems and increasing pressure on supporting infrastructure. This is due to a warming ocean and increases in the frequency and intensity of storms leading to accelerated erosion, storm surges and flooding. Building resilience and adaptive capacity in such social-ecological systems, in the context of change, involves learning to live with change and uncertainty, fostering exchange of knowledge, and taking advantage of the opportunities for renewal. In its initial phases the Partnership for Canada-Caribbean Community Climate Change Adaptation (ParCA) research sought to integrate scientific and local knowledge to understand the multi-scale socioeconomic, governance and environmental conditions that shape vulnerability and capacity to adapt to climate change. Associated community visioning processes and design charrettes build on community assets to develop and evaluate local adaptation options that address community needs and cultural values. In its final phase it is seeking to mobilize knowledge to foster resilience to change in coastal communities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".