Navigating Nature-Based Coastal Adaptation through Barriers: A Synthesis of Practitioners’ Narrative from Nova Scotia, Canada
Bibliographic record
Abstract
Nature based coastal adaptation (NbCA) sustainably helps minimize sea-level rise impacts, using and enhancing the natural capacity of coastal ecosystems. Despite its relative advantages over conventional hard protection infrastructure, the implementation of NbCA is challenged by diverse barriers, many of which cannot be overcome in the absence of appropriate policy directives. This paper draws on organizational practitioners’ case study narratives collected from six NbCA projects planned and/or implemented in Nova Scotia, Canada, to answer how the implementation of NbCA approaches can be navigated through adoption barriers. Results reveal that institutional and psychological barriers dominate, and they also show path-dependency. In addition, barriers are often influenced by the biophysical properties of a restoration site. To navigate through barriers, it is important to identify policy opportunities and redistribute roles and responsibilities. Organizational knowledge creation through partnership and community engagement are two other strategies required for the successful implementation of NbCA.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".