When Water Quality Crises Drive Change: A Comparative Analysis of the Policy Processes Behind Major Water Contamination Events
Bibliographic record
Abstract
Abstract The occurrence of major water contamination events across the world have been met with varying levels of policy responses. Arsenic—a priority water contaminant globally, occurring naturally in groundwater, causing adverse health effects—is widespread in Bangladesh. However, the policy response has been slow, and marked by ineffectiveness and a lack of accountability. We explore the delayed policy response to the arsenic crisis in Bangladesh through comparison with water contamination crises in other contexts, using the Multiple Streams Framework to compare policy processes. These included Escherichia coli O157:H7 and Campylobacter in Walkerton, Canada; lead and Legionella in Flint, Michigan, USA; and chromium-6 contamination in Hinkley, California, USA. We find that, while water contamination issues are solvable, a range of complex conditions have to be met in order to reach a successful solution. These include aspects of the temporal nature of the event and the outcomes, the social and political context, the extent of the public or media attention regarding the crisis, the politics of visibility, and accountability and blame. In particular, contaminants with chronic health outcomes, and longer periods of subclinical disease, lead to smaller policy windows with less effective policy changes. Emerging evidence on health threats from drinking water contamination raise the risk of new crises and the need for new approaches to deliver policy change.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".