Mapping risks of water injustice and perceptions of privatized drinking water in the United States: A mixed methods approach
Bibliographic record
Abstract
About 2 million people in the United States do not have access to running water or indoor plumbing in their homes. In addition, 30 million more Americans live where water systems operate unsafely. More still could have access to clean and safe drinking water but cannot afford to pay for it. Water privatization has been proposed as both a solution to and an exacerbator of these challenges, but its potential consequences have not been investigated on a national scale. Data from the US Environmental Protection Agency's Safe Drinking Water Information System and the US Center for Disease Control's Environmental Justice Index were used to assess the spatial distribution of water injustice hotspots, water system violations, and water system ownership. These data were merged with a nationally representative survey of US residents that measured how people perceive their water across different water injustice indicators. Results indicated that water system violations were not randomly distributed across the United States and risks of exposure to water injustice appeared to cluster in certain locations as hotspots. Clusters of water system violations were spatially associated with private water system ownership. Hotspots of water injustice were more often surrounded by counties with low proportions of privately owned water systems than counties with high proportions. Results also suggested that individuals living in areas with higher water injustice perceived their water as lower quality and less reliable. Water system ownership moderated this relationship. Recommendations for policymakers are discussed, including how to build collaborative decision-making processes that account for both objective and subjective measures of water injustice.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".