Making Waves: A justice-centred framework for wastewater-based public health surveillance
Bibliographic record
Abstract
Since 2020 wastewater-based surveillance has quickly been established as an effective and cost-efficient tool for monitoring public health. In this Making Waves article, we argue that these programs must be grounded in principles of justice to achieve global water and health equity. Ethics initiatives to date have focused primarily on privacy, legality, and institutionalised research reviews, often, if not exclusively, in North America and Western Europe. We draw from our interdisciplinary, multisectoral, and international expertise and experience to develop a justice-centred framework for wastewater-based surveillance. First, we identify common concerns across diverse surveillance programs including: defining community, transparency and accountability, and uneven geographies. Second, we draw on political theorist Nancy Fraser's framework of justice to evaluate site-specific practices identifying maldistribution, misrecognition, and exclusion. We suggest that Fraser's framework offers a common approach for evaluating just outcomes rather than specific regulations for governing wastewater surveillance across different and unequal contexts.
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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 | 0.159 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".