When Science Becomes Embroiled in Conflict: Recognizing the Public’s Need for Debate while Combating Conspiracies and Misinformation
Bibliographic record
Abstract
Most democracies seek input from scientists to inform policies. This can put scientists in a position of intense scrutiny. Here we focus on situations in which scientific evidence conflicts with people's worldviews, preferences, or vested interests. These conflicts frequently play out through systematic dissemination of disinformation or the spreading of conspiracy theories, which may undermine the public's trust in the work of scientists, muddy the waters of what constitutes truth, and may prevent policy from being informed by the best available evidence. However, there are also instances in which public opposition arises from legitimate value judgments and lived experiences. In this article, we analyze the differences between politically-motivated science denial on the one hand, and justifiable public opposition on the other. We conclude with a set of recommendations on tackling misinformation and understanding the public's lived experiences to preserve legitimate democratic debate of policy.
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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 | 0.045 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.028 | 0.069 |
| Scholarly communication | 0.037 | 0.032 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".