Translatability of water governance experiments across settings and scales
Bibliographic record
Abstract
Adaptive governance requires institutional capacity to coordinate responses to environmental problems at appropriate scales and utilizes networks for information sharing. This implies a capability to translate successful governance experiments from one social-ecological setting to another. Yet, translating lessons learned from case studies in adaptive water governance to other settings is all but straightforward. Watershed condition is a cumulative result of upstream ecological factors as well as land use decision-making processes, which may involve diverse stakeholders and multiple, nested levels of government. The relationships between site-specific land management decisions and water-related ecosystem services not only vary by location, but are further complicated by biogeochemical flows, ecological interactions, and social-ecological trade-offs. We view this governance challenge from a biophysical science perspective, highlighting the need to focus on translatability of governance approaches such that land use decision-making processes can better fit the dynamic, multidimensional, spatially continuous nature of riverine networks. To learn from a previous attempt to translate a successful water governance experiment across social-ecological settings, we investigated a case study of riverside area management in Washington State, northwestern USA. As participants in an agency-led workshop, we observed particular challenges in coordinating riverside management recommendations across a spatially variable social-ecological landscape. To clarify potential steps for translating riverine policy experiments, we intersected ecological understanding with adaptive governance scholarship. Using the case study as an example of the challenges of translating a policy experiment, we reviewed the ecological, management, and adaptive governance literatures to identify four elements of translatability: (1) a cross-sectoral, multiscale understanding of the shared goals or future desired state of the system; (2) quantified functional relationships between measurable site-scale features and ecosystem functions related to the shared goals; (3) definition frameworks to relate ecological concepts to the levels of potentially networked governance; (4) mapping strategies to visualize emerging networked governance in spatial context. We reviewed definitions pertaining to riverside areas and arranged them along a concept-application spectrum to provide a framework to relate ecological knowledge to the levels of potentially networked riverine governance. We mapped the spatial footprints of related policies nested within areas of similar ecological landscape characteristics to show spatial patterns that could inform translation of governance experiments in empirical context. We then discussed the role of translatability in adaptive water governance. We conclude with recommendations for considering the translatability of adaptive water-governance experiments and identifying potential opportunities to leverage existing ecological and institutional relationships to improve cross-scale fit with ecosystems across heterogeneous landscapes.
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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 | 0.220 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.012 | 0.030 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".