Growing pains: Overcoming barriers to nature-based coastal adaptation projects through collaboration
Bibliographic record
Abstract
Coastal climate impacts have evolved so that the solutions that infrastructure managers have historically used to adapt to flooding may no longer be sufficient. Consequently, many have begun to consider alternatives to conventional grey infrastructure, including nature-based coastal adaptation (NBCA) projects. NBCA projects, a subset of nature-based solutions, have several characteristics that provide additional benefits compared to grey infrastructure, but those same characteristics and the novelty of the solutions can make NBCA challenging to implement. To overcome those challenges, collaboration has been suggested as a way of drawing on multiple perspectives, skillsets, and knowledge bases. To examine the ability of collaboration to advance NBCA projects, I conducted a case study of the Boundary Bay Living Dike (BBLD), one of the first NBCA projects in the Canadian province of British Columbia. I conducted interviews with 32 individuals who had been directly involved with the BBLD project to understand participants’ perspectives on 1) the barriers to BBLD, and 2) the ways in which collaboration interacted with those conditions. In examining the interview data and building on the barriers to adaptation literature, I developed a conceptualization of enabling conditions: factors that help or hinder a project based on the degree to which they are present, the timeframe over which they are available, the presence of actors who can make use of them, and the conditions’ interactions with each other. In doing so, I found that the most significant hindering factors were institutional (such as jurisdiction and mandate, assumptions and paradigm, and regulations) and systemic (influenced by conditions such as the Covid-19 pandemic and high inflation). I then examined the ways in which collaboration interacted with those conditions by applying a framework of action-based roles to characterize the collaborative process surrounding the BBLD, finding that collaborators were able to both support the project within formal structures and fill the gaps left by systems not designed to accommodate NBCA. These findings contribute conceptually to the barriers to adaptation literature, and practically to both those looking to implement NBCA and those with the ability to develop systems to enable them.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".