The Essential Role of Wetland Restoration Practitioners in the Science-Policy-Practice Process
Bibliographic record
Abstract
We conducted a “living laboratory” study using a holistic transdisciplinary approach to demonstrate how new scientific tools and policy instruments could be mobilized to achieve wetland restoration goals. Our living laboratory was situated on the prairie pothole landscape in the province of Alberta, Canada, where policies require the replacement of lost wetland habitat. We created tools to map ditch-drained wetlands and to measure their functions in terms of hydrological health, water quality improvement, and ecological health to optimize targeting of wetland restoration sites. We also tested new policy instruments to incentivize private landowners to restore ditch-drained wetlands. However, we arguably failed in the implementation of the restoration program due to barriers that severely limited landowner participation, resulting in only a small number of wetlands being restored. Despite strength in science and a profound understanding of the policy, on-the-ground restoration work was stalled due to the interactive effects of environmental, social, economic, and political barriers. We discovered that despite our focus on overcoming the science-policy gap, it is the practice realm that requires more attention from both scientists and policy makers engaged in wetland restoration activities. Generally, the tools we developed were irrelevant because of complex interactions between actors and barriers within the policy, governance, and site-specific contexts that limited the use and application of the tools. Our living laboratory highlights the risks of engaging in use-inspired research without having a clear understanding of the actors and the interacting contexts that influence their behavior, motivations, and risk tolerance. Informed by our experiences, we offer key considerations for better engagement of practitioners in the design and implementation of wetland restoration programs.
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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 | 0.170 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.031 | 0.078 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".