Bibliographic record
Abstract
Wastewater analysis and surveillance are well-established practices whose use has dramatically expanded during the COVID-19 pandemic. In this article, we argue that the extraction of diverse types of data from wastewater is part of the larger phenomenon of ‘datafication’. We explore the evolving technologies and uses of wastewater data and argue that there are insufficient legal and ethical frameworks in place to properly govern them. We begin with an overview of the different pur- poses for wastewater data analyses as well as the location and scale of collection. We then consider legal and ethical principles and oversight frameworks that shape current approaches to wastewater collection. After situating wastewater collection within its particular civic context, we argue in favour of greater engagement with legal and ethical issues and propose doing so through a civic perspective. Our paper concludes with a discussion of the normative shifts that are needed and how we might achieve these.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.104 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.011 | 0.062 |
| Scholarly communication | 0.029 | 0.036 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".