Institutional barriers and facilitators in green infrastructure implementation in British Columbia and Washington State
Bibliographic record
Abstract
Soft shore protection measures, when applied in appropriate scales and locations, can offer a cost-effective, dynamic, safe and multi-functional coastal protection option and sea level rise adaptation strategy. Preserving existing natural buffers at the shore and responsibly creating new ones can help communities with limited resources to tackle challenges of climate change impacts. But why soft shore protection measures are widely developed and implemented in Washington State, but face barriers in British Columbia? Institutions and their hierarchical structures play critical roles in sea level rise adaptation and flood risk reduction actions. They influence the decision to adopt one strategy or policy over another one, such as using soft shore protection measures instead of traditional hard structures. Even though soft shore protection has gained attention for its role in sea level rise adaptation, flood risk reduction and providing multiple ecosystem services, compared to the hard structures its legal and regulatory basis has developed at various significance levels in different places. Even within the same geographical and ecological region such as the Salish Sea, the presence of two countries, Canada and the United States, proves how institutions and their hierarchical structures impact the implementation of soft shore protection across the region. In order to foster the development and implementation of soft shore protection, organizational gaps and limitations, as well as drivers and opportunities should be identified. Therefore, this presentation addresses the following question: what are the institutional barriers and facilitators affecting soft shore protection implementation? Document analysis and semi-structured interviews with local, regional and provincial/state government experts are used to collect data. The results of this project highlight the institutional barriers and facilitators that are rooted in the governance systems in British Columbia and Washington State, and brings forward recommendations to fill institutional gaps and reduce limitations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".