Coastal resident perceptions of nature‐based adaptation options in Nova Scotia
Bibliographic record
Abstract
Abstract Climate change risks to coastal communities may overwhelm current management strategies. The emergence of nature‐based solutions could provide alternative approaches for climate adaptation; however, studies on their public acceptability are limited. This research focuses on the human dimensions of nature‐based coastal adaptation solutions. The research sought to understand the kind of environmental changes participants were experiencing in their coastal communities of Nova Scotia and what management responses they observed being taken, if any. Online focus groups were held with coastal property owners in Nova Scotia to understand how they assess coastal risks and four approaches to nature‐based coastal adaptation: living shorelines, accommodation, retreat, and dyke realignment to make space for wetland restoration. Results revealed ongoing trust in traditional hard‐line approaches, but also interest in knowing more about nature‐based options. There was general support for living shorelines, albeit with scepticism; a concern that accommodation is just a “band‐aid” approach; resistance to retreat, despite general recognition of its future utility; and a lack of understanding of dyke realignment. The successful implementation of nature‐based coastal adaptation approaches will require more evidence of their viability, better options for financing them, and engagement with communities around the best‐fit alternatives for them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".