Rethinking Water Governance: Moving Beyond Water-Centric Perspectives in a Connected and Changing World
Bibliographic record
Abstract
From the “water-centric” perspective that is common within the world’s large and diverse water community, water is of central importance, and improving water governance is self-evidently essential. Some water problems can be addressed using water-centric approaches such as watershed management. Unfortunately, evidence is mounting that suggests that many other water problems cannot because their causes and drivers, at scales from local to global, are partly or wholly external to those traditionally considered within the water sector. Water governance in these cases needs to better account for a range of external connections that strongly influence water-related outcomes of concern and contribute to governance failures. These connections frequently manifest through external actors, drivers, and institutions. We address this issue by critically reflecting on the limitations of water-centric perspectives; surveying the water governance literature to identify external connections that can influence water governance; examining the extent to which four major approaches address actors, drivers, and institutions that connect water governance to other sectors and decision making situations (Integrated Water Resources Management, water security, water-energy-food nexus, water resilience); and considering key conceptual and practical challenges of moving beyond water-centric approaches where this is warranted. Building on emerging thinking within the water community, we propose that key open questions requiring urgent attention relate to reconciling water-centric and non-water-centric approaches, thinking critically about boundary judgments, and re-thinking conceptual and practical approaches to water governance to better account for external connections. The article contributes to emerging conversations about the future of water governance in an increasingly complex, connected and rapidly changing world.
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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 | 0.036 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.104 |
| Scholarly communication | 0.025 | 0.056 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".