Justice and Moral Economies in Modular, Adaptive, and Decentralized (MAD) Water Systems
Bibliographic record
Abstract
Scholars and practitioners now acknowledge that “MAD Water” systems (modular, adaptive, decentralized engineered infrastructures) will expand to meet human water needs under future climate change, migration, and urbanization scenarios. Yet social science research on existing MAD water infrastructures documents how the use and deployment of such systems often undermines water justice. Here we posit that identifying and analyzing moral economies for water provides one approach for scholars to understand—and possibly predict—when and why justice norms in MAD water systems break down or become unstable. Moral economies are institutional arrangements in which people’s shared ideas of justice normatively shape how they distribute and exchange basic resources. We review the concept of moral economies, explain an operational framework for researching moral economies, and illustrate how moral economies function within three already-operating MAD water systems today: water sharing arrangements, informal water vending markets, and small-scale water commons. We argue that when moral economies are embedded and operating successfully within MAD water systems, they can create check-and-balance mechanisms against injustice. But when moral economies are absent or failing in MAD water systems, water injustices often prevail. As such, the moral economies framework provides not only a tool for analysis, but also a possible language and pathway forward toward organizing for justice in MAD water systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".