We must account for the results of water governance to deliver the SDGs and beyond
Bibliographic record
Abstract
The crisis of water governance is one of results. This perspectives paper argues that we need to move beyond calling the global water crisis a crisis of governance. Instead, we must focus more on—and account for —the results of water governance innovations that increasingly blend public, private, and community roles. Drawing on the insights from a five-year doctoral training network on water governance, we illustrate the need for better accounting and assessment of water governance. We anchor our argument and recommendations in illustrative examples of governance innovations in Sub-Saharan Africa where linking water governance with results is necessary but challenging. After illustrating these challenges, we outline recommendations for interdisciplinary water scholars in five steps: (1) defining effective water governance in terms of the results they deliver, (2) measuring the impacts of water governance innovations by linking governance mechanisms and outcomes, (3) empowering decision-makers by examining the effects and effectiveness of water governance through place-based, science-policy partnerships, (4) creating water governance data observatories that combine data and narratives to track changes, make inequalities visible, and guide tradeoffs, and (5) broadening our typologies of water governance to better capture institutional diversity and improve ‘fit’ between water governance and outcomes. Together these steps help to foster learning and action and build the credibility of water governance research. A new wave of water governance scholars is ready to move beyond paradigms and principles to action and outcomes. Accelerating the transition to this next generation can inform debates with evidence and narratives that empower people, communities, governments, and companies to govern water better.
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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 | 0.113 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.028 | 0.054 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".