Water Stress, Peri-Urbanization, and Community-Based Water Systems: A Reflective Commentary on the Metropolitan Area of Mexico City
Bibliographic record
Abstract
With a population of over 22 million, Mexico City's metropolitan area is facing enormous water security challenges. Its supply heavily relies on overdraft of groundwater and import from neighboring basins, leading to problems such as subsidence and raising concern over its sustainability. The impacts of the water stress in Mexico City are highly unequal across the metropolitan area and particularly severe in low-income peri-urban neighborhoods. This paper will first review the current water stress in Mexico City, its impacts on vulnerable communities, as well as some existing technical and institutional approaches aiming to tackle these challenges. We will then focus on the community-based water management systems in Mexico City's peri-urban areas, based on the case of Tecámac, one of the 59 municipalities that form the conurbation area. We will discuss the local water politics in the municipality, the historical evolution of the community-based water system, SAPTEMAC, as well as its current agenda. This essay highlights the importance of incorporating the community-based water systems in the development toward a solution to the water crisis in megacities like Mexico City: not only do they serve as provider of potable water to a considerable number of households, but they also represent a collective resistance against the speculation-driven (peri) urbanization and can make substantial contribution to the promotion of a comprehensive water reform in the country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".