(De)centralized Water Futures: Key Dimensions of Infrastructure, Governance, and Operations
Bibliographic record
Abstract
Water system centralization and decentralization have variously been promoted as key to achieving household water security and Sustainable Development Goal 6.1. We argue that the lack of specificity with which scholars and practitioners use the terms centralization and decentralization limits our understanding of different water system configurations and their impacts. In this Primer, we provide a framework for thinking about levels of (de)centralization across three linked system dimensions: infrastructure, governance, and operations and maintenance. We encourage those analyzing water systems to characterize (de)centralization with respect to these multiple dimensions, as well as the system's broader political-economic and hydro-climatic contexts. Emphasizing the importance of delineating the scale of analysis, we highlight distinct system configurations and the prevalence of hybridity. Increased specificity about dimensions and scale can clarify how the character of, or changes to, a given system impact users, which is critical to assessing their implications for water security, sustainability, and equity. We conclude with recommendations for future research to analyze the opportunities and challenges associated with different water system configurations. This article is categorized under: Human Water > 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".