Social influence shapes adaptive water governance: empirical evidence from northwestern Pakistan
Bibliographic record
Abstract
Social-ecological change has placed unprecedented stress on water resources throughout the world. This has driven water users to employ a diverse range of adaptation strategies and necessitates new governance structures, such as adaptive water governance (AWG), which have the capacity to manage resources in the midst of uncertainty and complexity. As such, AWG has the potential to support household adaptation strategies; however, little empirical work has been done to identify the factors that facilitate the emergence of AWG. To address this gap, we conducted a household survey of 448 households in northwestern Pakistan, a post-conflict, water-scarce area where adaptive governance is needed to support rural livelihoods in the midst of numerous socioeconomic and environmental transformations. Indeed, we found that households in our study area perceived a range of changes to the water system, including but not limited to declines of fish populations, decreased quality and amount of river water, and an increase of local tourism. Respondents reported a range of adaptation strategies including increasing agricultural inputs, planting new crop varieties, and changing their domestic water supply system. In some cases, households employed these adaptation strategies despite economic barriers, and although many were willing to go against friends’ and community leaders’ opinions to adapt, and they were less likely to counter the opinions of family members. This reveals that households negotiate multiple factors in their decisions to adapt to social-ecological change; as such, there is a great need for flexible and collaborative governance systems such as AWG to support this complexity in household adaptation decision making. Further, we argue that the varying roles of social influence should be considered to align governance structures with household decision-making processes. Thus, we suggest that AWG will be more likely to emerge when decision makers involved in water management draw on existing informal institutions and cross-sectoral collaboration to reflect the complex ways water users adapt to social-ecological change.
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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 | 0.000 | 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".