Public Reactions to Risk Messages Communicating Different Sources of Uncertainty: An Experimental Test
Bibliographic record
Abstract
There is an abundant literature on the challenge of integrating uncertainties in experts’ risk assessments, but the evidence on the way they are understood by the public is scarce and mixed. This study aims to better understand the effect of communicating different sources of uncertainty in risk communication. A causal design was employed to test the effect of communicating risk messages varying in type of advisory warning (no risk and suggests no protective measure, or risk and recommends a protective measure) and sources of uncertainty (no uncertainty, divergence between experts, contradictory data, or lack of data) on public reactions. Participants from the general public (N = 434) were randomly assigned to read and react to variants of a fictitious government message discussing the presence of a new micro-organism found in tap water. Multiple analysis of variance showed that to report uncertainty from divergence between experts or from contradictory data reduced the adherence to the message, but not to mention the lack of data. Moreover, the communication of diverse sources of uncertainty did not affect trust in the government when the advisory warning stated there was a risk and recommended a protective measure. These findings have important implications for risk communication.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".