How Bridging Approaches Further Relationships, Governance, and Ecosystem Services Research and Practice
Bibliographic record
Abstract
Understanding environmental governance empowers researchers and practitioners alike to work towards solutions that improve both environmental and human well-being outcomes. Collaborative, iterative approaches to governance use bridging approaches such as translational ecology, boundary work, and ecosystem services. The US Environmental Protection Agency's Office of Research and Development worked with a variety of collaborators to implement six multi-year coordinated case study research projects. The research projects were designed to support agency collaborators spanning different geographies, ecosystems, and environmental management decision contexts, and to demonstrate that different tools, approaches, and ecosystem service foci can enhance coastal and other water resource sustainability. To better understand the iterative and collaborative nature of the cases and collaborations, researchers conducted an analysis of the comparative case studies based on Williams' (2018) and Ostrom's (1994, 2009) frameworks. The team identified (1) who participated in the processes; (2) what the programs and goals were; (3) where programs worked and their resources; and (4) the resulting outcomes. We demonstrate that stakeholder participation and outcomes look different within different projects, and we conclude that relationships, focus on place, and common goals produce the most impactful results.
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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 | 0.063 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.028 | 0.047 |
| Open science | 0.004 | 0.029 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".