Constructing Scientific Authorities: Issue Framing of Chlorinated Disinfection Byproducts in Public Health
Bibliographic record
Abstract
The practice of chlorine disinfection of drinking water to reduce microbial risks provides substantial benefits to public health. However, increasing concern around potential risks of cancer associated with exposure to chlorinated disinfection byproducts confuses this issue. This article examines the science agenda regarding chlorinated disinfection byproducts (CDBP) and cancer in Canada and the United States, focusing on the social construction of scientific knowledge claims and evidence. Data for this analysis were obtained from published documents as well as from in-depth interviews with epidemiologists and toxicologists centrally involved with the issue in both countries. Results of the analysis suggest that toxicological scientists want to close the door on the "chloroform issue" due to increasing evidence that chloroform is safe at low doses, because epidemiological scientists can no longer move forward the cancer science until significant improvements can be made in assessing human exposures, and because the scientific foci of research on DBP have shifted accordingly. Further, a distinction emerges in terms of how scientific uncertainties are interpreted when they cross-cut disciplines in the context of human health risk assessment. We suggest this tension reflects a balance of how uncertainty and authorities are managed in a mandated science-policy domain. Sufficient evidence was provided to keep the DBP issue on the regulatory agenda and to generate additional research, yet authorities and concomitant interpretations of uncertainty were contested. Such science generation and contestation inevitably influences complex risk assessment processes with respect to what water-related health risks are addressed and how.
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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 | 0.100 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.023 | 0.070 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.012 | 0.012 |
| 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".