Exploring Scientists’ Values by Analyzing How They Frame Nature and Uncertainty
Bibliographic record
Abstract
Several scholars have proposed that values embedded in science are a central reason why more research does not necessarily resolve scientific controversies around complex environmental issues. In the Capital Regional District, British Columbia, Canada, scientists have positioned themselves for and against the construction of a wastewater treatment plant in a debate framed as purely technical. This study explores the link between the scientists' positions in the debate and the way they, in their scientific publications, portray nature and environmental risks. We performed a qualitative content analysis of peer-reviewed publications by scientists who have publicly taken opposing positions in the controversy. We found that scientists against treatment predominantly frame nature as tolerant, up to a limit, to disturbances and potential risks, and they seem to embrace a view of science as capable of reducing uncertainties. In contrast, scientists in favor of treatment predominantly portray nature as fragile, particularly toward human-mobilized environmental risks and they commonly present scientific uncertainty as worrisome based on potentially harmful consequences. Our study suggests that value-laden perspectives impact scientists' positions 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 | 0.049 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".