Science and values in a wastewater treatment controversy
Bibliographic record
Abstract
Different scholars hold that values embedded in science are a central reason why more research does not solve scientific controversies related to complex environmental issues. In the Capital Regional District, British Columbia, Canada, different scientists have positioned themselves for and against the construction of a wastewater treatment plant in a debate framed as purely technical. This study investigates how scientists with opposing positions view nature and consider uncertainties, as well as what are their assumptions. I analyzed peer-reviewed publications of scientists who have positioned themselves publicly on either side of the controversy. Then, I conducted four semi-structured interviews with two scientists from each side. I found that scientists against treatment framed nature as tolerant to disturbances up to a limit and believed that scientific research can eliminate uncertainties. They assumed that sewage is not a risk because it is composed mainly of nutrients and traditional wastewater quality measurements have not shown evidence of harm. In contrast, scientists in favor of treatment portrayed nature as fragile and judged uncertainty as worrisome based on potentially harmful consequences. They also considered the sewage a risk because of the chemical substances it contains which are not included in traditional measurements. This study suggests that value-laden perspectives impact scientists’ positions and recommendations even in a seemingly technical controversy.
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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 | 0.046 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.041 | 0.112 |
| Scholarly communication | 0.032 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| 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".