Balancing water needs and well-being: bringing social-cultural values into environmental flows using a DPSIR framework
Bibliographic record
Abstract
Bringing social and cultural dimensions into environmental flows (eflows) is critical for sustainable river management, yet structured methods for this process are lacking. We tested the efficacy of a Driver-Pressure-State-Impact-Response (DPSIR) framework, quantified with Fuzzy Cognitive Mapping (FCM), to bridge ecological processes and social-cultural-spiritual values for the regulated Wolastoq | Saint John River | fleuve Saint-Jean, a large transboundary watershed in Maritime Canada. We integrated data from expert-led workshops, which produced 69 refined flow-ecology hypotheses, and a public survey on social-cultural-spiritual connections to the river. The resulting semi-quantitative model revealed a complex network of 39 social-ecological nodes with 941 positive and negative connections and provided a visual map of these connections. Network analysis identified flow variability (an environmental stressor) and peace + tranquility (a social-cultural-spiritual state) as the most significant nodes within the map, acting as critical bridges between the environmental and human domains. The framework explicitly linked physical processes and flow management actions, like hydropeaking, to tangible impacts on ecosystem health (e.g., water quality and biodiversity), recreational access, and community well-being, while also highlighting a potential feedback loop where a sense of peace also promotes environmental stewardship. Our findings demonstrate that the DPSIR-FCM approach is a powerful tool for creating a holistic, transparent, and socially defensible foundation for eflows management. It translates complex social-ecological interactions into an actionable decision-support tool that prioritizes management decisions that promote inclusive, evidence-based water governance.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".