Scientific uncertainty in media content: Introduction to this special issue.

This introduction sets the stage for the special issue on the public communication of scientific uncertainty that follows by sketching the wider landscape of issues related to the communication of uncertainty and showing how the individual contributions fit into that landscape. The first part of the introduction discusses the creation of media content as a process involving journalists, scientific sources, stakeholders, and the responsive audience. The second part then provides an overview of the perception of scientific uncertainty presented by the media and the consequences for the recipients' own assessments of uncertainty. Finally, we briefly describe the six research articles included in this special issue.

The influence of weight-of-evidence strategies on audience perceptions of (un)certainty when media cover contested science.

Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to "get both sides of the story" even when there is little debate in the scientific community about which truth claim is most valid. In this study, we look at whether highlighting the way in which experts are arrayed across truth claims-a strategy we label "weight-of-evidence reporting"-can attenuate heightened perceptions of uncertainty that can result from coverage of conflicting claims. The results of our study suggest weight-of-evidence strategies can indeed play a role in reducing some of the uncertainty audiences may perceive when encountering lop-sided truth claims.

Studying heuristic-systematic processing of risk communication.

Using a model of risk information seeking and processing developed by Griffin, Dunwoody, and Neuwirth (1999), this study looks at predictors of the processing strategies that people apply to health risk information. Specifically, this article focuses on one relationship within the model--the relationship between perceived amount of information needed to deal with a risk and heuristic-systematic processing. Perceived amount of information needed refers to the gap between one's understanding of a risk and the level of understanding that one needs in order to make a decision about that risk. Building on the work of Chaiken (cf. 1980), the Griffin et al. model predicts--and finds--that the larger the gap, the more likely one will process information systematically. The study employs a novel measure of information processing in a survey setting by sending actual information to participants and then asking them how they attended to it; the researchers evaluate this strategy. Finally, the researchers discuss how these findings might help agencies and practitioners create more effective risk messages.

Attributions in explanations of risk estimates.

In the spring of 1993, nearly 40 percent of Milwaukee-area residents experienced a nationally publicized outbreak of cryptosporidium, a parasite that infested the metropolitan drinking water supply. Using open-ended survey data gathered from 610 adult residents in the wake of that outbreak, this study looks at factors related to the ways in which people make sense of their quantitative personal risk estimates. The concepts of informal reasoning and attribution aided this endeavor. Analysis of open-ended comments about the risk of getting ill from a waterborne parasite indicated that explanations of personal risk were consistent with predictions made by attribution theory. Good outcomes, which included having remained healthy during the outbreak, were associated with a greater likelihood that respondents would attribute causation to themselves, while one specific bad outcome, having experienced the illness, was associated with a greater likelihood that respondents would attribute causation externally. This study also examined predictors of whether respondents employed probabilistic language in those attributions. Analysis indicated that income was positively related to the use of probability-oriented language, while age and race were negatively related to the use of such language (i.e., persons of color and older individuals were less likely to use such language).