Decentralized Water Infrastructure and Peri-Urban Water Security: Policy Challenges and Opportunities in Rainwater Harvesting Initiatives in Mexico City
Bibliographic record
Abstract
Like other megacities worldwide, Mexico City faces significant water security challenges in a context of rapid urbanization and climate change. The impacts of water stress are particularly harsh in the city’s unplanned peri-urban settlements, where reliable centralized municipal water is often unavailable. In response, decentralized solutions such as rainwater harvesting have become an important tool for a variety of stakeholders to improve access to water and sanitation and to enhance climate resilience. However, many details about how best to ensure safe management, safe water quality, and long-term sustainability to these systems remain unresolved. Drawing from our field observations and insights from local practitioners, in this article we demonstrate the varying attitudes, experiences, and perspectives with rainwater harvesting systems. We discuss the opportunities and barriers to the long-term uptake of decentralized water technologies and examine approaches to responsibly integrate water technologies and innovations, community participation, and water rights. We will also discuss the role of various stakeholders in creating an enabling environment for community-based water innovations. This article highlights the importance of a long-term and holistic perspective to decentralized infrastructure initiatives and calls for evidence-based innovation, which integrates citizen participation/ownership, public awareness, and localized risk management.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".