Building resilience to extreme weather and climate events in the rural water and wastewater sectors
Bibliographic record
Abstract
Abstract Extreme weather and climate events pose significant risks to rural water and wastewater systems. We examine the vulnerability of the water sector to weather and climate extremes in rural, predominantly Indigenous and underserved coastal areas and analyze how networks support resilience. Drawing on the analysis of 39 web‐based questionnaire responses and 19 interviews with rural water and wastewater managers and service providers in southern Louisiana and western Alaska, this article reports a range of interrelated historical, environmental, and social factors that influence vulnerability to extreme weather events. Formal and informal social networks serve multiple roles in building resilience. These roles include building technical and financial capacities, supporting emergency response and operational‐ to long‐term planning, fostering data collection and monitoring, supporting information sharing and innovative research, and providing institutional support. Results from this research enrich our understanding of the social, relational, and networking processes that condition community resilience to extreme weather events.
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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 | 0.003 | 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 | 0.000 | 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 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".