Affect-inducing risk communication: current knowledge and future directions
Bibliographic record
Abstract
Affect appears to have a central role in people’s risk perception and decision-making. It is, therefore, important that researchers and communicators know how risk communication can induce affect or more specific emotions. In this paper, several studies that examined affect-inducing cues presented in and around risk communication are discussed. We thereby distinguish between integral affect induction, meaning through the risk message, and incidental affect induction, which occurs unintentional through the risk communication context. The following cues are discussed: emotion induction, fear appeals, outrage factors, risk stories, probability information, uncertainty information and graphs and images. Relatively few studies assessed the effect of their risk communication material on affect or specific emotions. Incidental affect induction appeared to occur more often than expected based on its factual content. Risk communication easily seems to induce affect incidentally and, thus, may be difficult to control. We, therefore, argue that incidental affect induction is more influential than integral affect induction. Implications for further research and risk communication in practice are given. Based on this overview, we strongly suggest considering and empirically assessing the affect-inducing potential of risk communication formats and content during their development and evaluation.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".