Institutional Innovation for Nature-Based Coastal Adaptation: Lessons from Salt Marsh Restoration in Nova Scotia, Canada
Bibliographic record
Abstract
Sea-levels have been rising at a faster rate than expected. Because of the maladaptive outcomes of engineering-based hard coastal protection infrastructure, policy makers are looking for alternative adaptation approaches to buffer against coastal flooding—commonly known as nature-based coastal adaptation (NbCA). However, how to implement NbCA under an institutional structure demonstrating ‘inertia’ to alternative adaptation approaches is a question that seeks scientific attention. Building on a case study derived from a highly climate-vulnerable Canadian province, this study shows how the entrepreneurial use of scientific information and institutional opportunities helped institutional actors overcome the inertia. Drawing on secondary document analysis and primary qualitative data, this study offers five key lessons to institutional actors aiming at implementing NbCA: (i) develop knowledge networks to help avoid uncertainty; (ii) identify and utilize opportunities within existing institutions; (iii) distribute roles and responsibilities among actors based on their capacity to mobilize required resources; (iv) provide entrepreneurial actors with decision-making autonomy for developing agreed-upon rules and norms; and (v) facilitate repeated interactions among institutional actors to develop a collaborative network among them. This study, therefore, helps us to understand how to implement a relatively new adaptation option by building trust-based networks among diverse and relevant institutional actors.
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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 | 0.002 | 0.004 |
| 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 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".