Moral economies for water: A framework for analyzing norms of justice, economic behavior, and social enforcement in the contexts of water inequality
Bibliographic record
Abstract
Abstract Over the past two decades, scholars have invoked E. P. Thompson's and James Scott's concept of a “moral economy” to explain how people mobilize notions of justice to make claims to water. We draw together 20 years of literature to assess the state‐of‐the‐art present in research on moral economies for water. We trace the historical foundations of the moral economies concept and its relevance to water; define the three basic components of a moral economy for water—(1) shared understandings of justice, (2) normative economic practices, (3) social pressure mechanisms—and provide examples of how they manifest globally. We then discuss how moral economies for water can cycle through four basic states—balanced struggle, intensified reaction, mass revolt, and collapse and dissolution—at different scales. We also explore the implications of the moral economies framework for key areas of current research on water: water sharing, water commons, water markets, and biocultural outcomes, and discuss the ways in which the moral economies framework dovetails with recent advances in water research, especially the economics of water and development. We argue that the moral economies framework is a powerful explanatory tool for understanding the relationships between ideas of water justice, economic behaviors, and mechanisms of social enforcement that complements other methodological approaches and theoretical perspectives. We envision moral economies for water as a field that can facilitate a range of norm‐based analyses of economic behavior and water justice, including across scales—from local to global—and in broad, integrative, multiscalar, and cross‐disciplinary ways. This article is categorized under: Human Water > Water Governance Human Water > Value of Water Human Water > Rights to Water
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".