Perspectives of Complexity in Water Governance: Local Experiences of Global Trends
Bibliographic record
Abstract
Those responsible for water governance face great complexity. However, the conceptualisations of what comprises that complexity have been broad and inconsistent. When efforts are made to address the complexity in water governance, it is unclear whether the problems and the related solutions will be understood across the actors and institutions involved. This paper provides a review of the literature focused on global water governance to discern core themes that commonly characterise discussions of complexity. It then considers how the consequences of these issues are manifested at the local scale through an examination of empirical research of the Murray-Darling Basin Authority and the Prachinburi River Basin Committee. The results demonstrate that a history of a technical, depoliticised discourse is often perceived to contribute to complexity. The consequence is that when a severe ecological disturbance occurs within a river basin with poorly understood causes, few tools are available to support river basin organisations to address the political nature of these challenges. Additionally, a lack of clear authority structures has been recognised globally, but locally this can contribute to conflict amongst the 'governors' of water. Finally, a range of contested definitions and governance frameworks exists that contributes to complexity, but confronting the diversity of perspectives can lead to ethical dilemmas given that the decisions will affect the health and livelihoods of basin communities.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".