From groundwork to implementation : a systematic review of coastal adaptation planning in Nova Scotia, Canada
Bibliographic record
Abstract
The Canadian province of Nova Scotia is at risk of coastal impacts resulting from the anticipated outcomes of climate change in the region. Sea level rise, along with more frequent and intense storms, will increase the instances and severity of coastal flooding events in the province, demonstrating the need for adaptation strategies in vulnerable coastal zones. The Municipal Climate Change Action Plans (MCCAPs), which were completed by municipalities in 2013 in response to a provincial mandate, have provided a foundation for adaptation planning in the province. Using the MCCAPs from 20 coastal communities as the basis for investigation, this study employs a mixed methods approach that includes content analysis, surveys, and expert interviews to evaluate the implementation of coastal adaptation actions that were identified as priorities in these plans. Two overarching research questions are asked: 1) what priorities for coastal adaptation were identified in the 2013 MCCAPs and which of these actions have been implemented since; and 2) What are the local factors that have influenced the success of implementation? Results demonstrate that the MCCAP mandate has been highly effective in stimulating coastal adaptation in the province, with nearly 75% of the 331 priority actions contained in these plans having been implemented to some degree. However, progress varies widely between communities and the data analysis suggests that both the characteristics of the communities and the characteristics of their planned actions are significant determinants of success. Previously developed theories on the influence of local policy factors in enabling successful adaptation are tested with mixed results. Some factors that are typically thought of as advantageous – dedicated funding, presence of a champion, and recent disasters – are shown to have no statistical influence on the success of plan implementation, whereas political continuity and public participation throughout the planning process have significant positive effects. Interviews with municipal representatives complement the data analysis with local context and perspective and offer insights for future adaptation planning in Nova Scotia and beyond.
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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 | 0.050 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.023 | 0.041 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".