Characterizing the social-ecological system for inland freshwater salinization using fuzzy cognitive maps: implications for collective management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it