Power research in adaptive water governance and beyond: a review
Bibliographic record
Abstract
Power dynamics are widely recognized as key contributors to poor outcomes of environmental governance broadly and specifically for adaptive water governance. Water governance processes are shifting, with increased emphasis on collaboration and learning. Understanding how power dynamics impact these processes in adaptive governance is hence critical to improve governance outcomes. Power dynamics in the context of adaptive water governance are complex and highly variable and so are power theories that offer potential explanations for poor governance outcomes. This study aimed to build an understanding of the use of power theory in water and environmental governance and establish a foundation for future research by identifying power foci and variables that are used by researchers in this regard. We conducted a systematic literature review using the Web of Science Core Collection and the ProQuest Political Science databases to understand how power is studied (foci, variables of interest, and methods) and which theories are being applied in the water governance field and in the environmental governance field more broadly. The resulting review can serve as a practical reference for (adaptive) water governance inquiries that seek to study power in depth or intend to integrate power considerations into their research. The identified power variables add to a much needed groundwork for research that investigates the role of power dynamics in collaboration and learning processes. Furthermore, they offer a substantive base for empirical research on power dynamics in adaptive water governance.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".