Integrating Stakeholder Knowledge Through a Participatory Approach and Semi-Quantitative Analysis for Local Watershed Management
Bibliographic record
Abstract
Watersheds are threatened by numerous issues, such as climate change, population growth, urban expansion, and industrial development. These issues are complex and interconnected, and effectively addressing them requires integrating the values, knowledge, and expertise of various governing bodies, local organizations, and community members, all of whom have their own viewpoints and priorities. The current study employs a participatory approach and systems lens to engage different stakeholders in the complexity of watershed issues and management approaches. Using participatory modeling and semi-quantitative scenario analysis techniques, the study identifies relationships among watershed values, challenges, and strategies as well as the dynamics of these relationships. A fuzzy cognitive map was developed, which consists of 53 nodes (i.e., 13 values, 12 challenges, and 28 strategies) and 113 connections. Biodiversity, mental health, and sense of place emerged as key values, as they exhibited high centrality values when analyzing the system, and challenges like invasive species and urban sprawl were found to exert considerable impacts on these values. Strategies such as establishing and expanding greenspace, community stewardship, and governance-based interventions were identified as critical for addressing watershed challenges and enhancing watershed values. The study identified a series of governance-based strategies that focus on resource allocation, participatory governance, and institutional collaboration to address watershed management challenges as well as a set of engagement-based strategies that focus on environmental communication and public awareness. The study demonstrates the potential that participatory modeling and semi-quantitative analysis techniques can have for integrating both tangible, measurable values and intangible, difficult-to-measure values into planning and policymaking. The research reinforces the idea that local governments play a critical role in fostering inclusive and collaborative watershed management strategies.
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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 | 0.052 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".