Characterizing the social-ecological system for inland freshwater salinization using fuzzy cognitive maps: implications for collective management
Bibliographic record
Abstract
Current regulatory tools are not well suited to address freshwater salinization in urban areas, and the conditions under which bottom-up management is likely to emerge remain unclear. We hypothesize that Elinor Ostrom’s social-ecological systems (SESs) framework can be used to explore how current understanding of salinization might foster or impede its collective management. We focus on the Occoquan Reservoir, a critical urban water supply in Northern Virginia, USA, and use fuzzy cognitive maps (FCMs) to characterize stakeholder understanding of the SES that underpins salinization in the region. Hierarchical clustering of FCMs reveals four stakeholder groups with distinct views on the causes and consequences of salinization, and actions that could be taken to mitigate salinization, including technological, policy, and governance interventions and innovations. Similarities and differences across these four groups, and their degree of concordance with measured or modeled SES components, point to actions that could be taken to catalyze collective management of salinization in the region.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".