How Can Aging Communities Adapt to Coastal Climate Change? Planning for Both Social and Place Vulnerability
Bibliographic record
Abstract
Coastal climate change in the form of rising sea levels and more frequent and extreme weather events can threaten community assets, residences, and infrastructure. This presents a particular concern for vulnerable residents—such as seniors aged 75 years and older. Our spatial study combines census area cohort population model projections, community asset mapping, and a municipal policy review with coastal sea rise scenarios to the year 2025–2026. This integrated information provides the basis to assess the vulnerability of our case study communities in Nova Scotia, Canada. Nova Scotia has the oldest population of any Canadian province, the majority of whom reside in coastal communities on the Atlantic, making it an ideal site for such analysis. Through this work we forward a useful decision-making support tool for policy and planning—one that can help coastal communities respond to the particular needs of seniors in rural areas and adapt to impacts of coastal climate change. Throughout we argue that social vulnerability must be considered alongside place vulnerability in the design of climate change adaptation and mitigation efforts. This is not just an issue for coastal communities, but for all communities facing the effects of extreme weather events.
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.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".