Charting Uncertainty in Science-Policy Discourses: The Construction of the Chlorinated Drinking-Water Issue and Cancer
Bibliographic record
Abstract
Drinking-water guidelines remain an ongoing issue within Canada, as elsewhere. Given recent epidemiological evidence concerning chlorinated disinfection byproducts in drinking water and cancer outcomes, some branches within Health Canada have been undertaking an extensive review of the issue. This paper examines what impact contested scientific authorities, as filtered through a regulatory agency, may have on the policymaking process in the setting of Canadian drinking-water guidelines. Using an agenda-setting framework, we rely on a textual analysis of a Health Canada expert panel report and a position paper written to accompany the panel report; Canadian print media translations of scientific evidence; and in-depth interviews with scientists (from the academy, industry, and government) and other interested stakeholders [for example, chlorine and water industry, and environmental nongovernmental organizations]. Through this analysis we reconstruct a discourse which suggests government-science in policy, rather than policymakers, is primarily and presently driving the issue. The issue itself appears to remain a debate which is largely over the strength of the scientific evidence from regulatory and public-health scientists (for example, in Health Canada), and their colleague research scientists (for example, leading researchers in the field). Although we argue that it is primarily cancer that drives the science-policy agenda, with respect to chlorinated drinking water, it is possible that reproductive effects are likely to be central to the debate for controlling chlorine use in the future.
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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 | 0.082 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.039 | 0.143 |
| Scholarly communication | 0.040 | 0.022 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 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".