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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".