Urban political ecologies of sewage surveillance: Creating vital and valuable public health data from wastewater
Bibliographic record
Abstract
Abstract With the outbreak of COVID‐19, wastewater surveillance for public health rapidly emerged and expanded globally. In this article we chart the variegated ecosystem of private firms that work closely with public and non‐profit entities to transform metabolic flows of sewage into vital and valuable bioinformation, thereby creating new multi‐institutional spaces of public health governance. We draw on literature in urban political ecology and political economy to ask: what are the emerging political economic actors, practices, and relations of wastewater surveillance? And how are emergent multi‐institutional public‐private partnerships and contracts transforming public health governance? To answer these questions, we use mixed qualitative methods to trace the field across North America, the Middle East and South Asia. Drawing on interviews, document and report reviews, financial reporting and observation at conferences, we find that these emerging public‐private partnerships present concerning transformations in health governance where profits displace public health needs, proprietary technologies blackbox public health decisions, and vulnerable populations are experimented on for prototyping technology. Our work contributes to renewed interest in urban political ecology's analysis of metabolism by tracing how, during health crises and their aftermath, public and private actors are together reconfiguring flows of waste, labour and technology to unlock new metabolic reservoirs of bioinformation.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".