Science for Policy: A Case Study of Scientific Polarization, Values, and the Framing of Risk and Uncertainty
Bibliographic record
Abstract
It is well documented that more research can lead to hardened positions, particularly when dealing with complex, controversial, and value-laden issues. This study is an attempt to unveil underlying values in a contemporary debate, where both sides use scientific evidence to support their argument. We analyze the problem framing, vocabulary, interpretation of evidence, and policy recommendations, with particular attention to the framing of nature and technology. We find clear differences between the two arguments. One side stress that there is no evidence that the present approach is causing harm to humans or the environment, does not ruminate on uncertainties to that end, references nature's ability to handle the problem, and indicates distrust in technological solutions. In contrast, the other side focuses on uncertainties, particularly the lack of knowledge about potential environmental effects and signals trust in technological development and human intervention as the solution. Our study suggests that the two sides' diverging interpretations are tied to their perception of nature: vulnerable to human activities versus robust and able to handle human impacts. The two sides also seem to hold diverging views of technology, but there are indications that this might be rooted in their perception of governance and economy rather than about technology per se. We conclude that there is a need to further investigate how scientific arguments are related to worldviews, to see how (if at all) worldview typologies can help us to understand how value-based judgments are embedded in science advice, and the impact these have on policy preferences.
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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 | 0.085 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.056 | 0.044 |
| Scholarly communication | 0.031 | 0.021 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.031 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 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".