Minimizing the Dangers of Air Pollution Using Alternative Facts: A Science Museum Case Study

A science museum exhibition about human health contains an exhibit that minimizes health impacts of air pollution. Relevant details, such as the full range of health risks; fossil fuel combustion; air quality statutes (and the local electrical utility’s violations of these statues), are omitted, while end users of electricity are blamed. The exhibit accomplishes this, not through outright falsification, but through selected “alternative facts” that change the focus and imply misleading alternate explanations. Using two classical rhetorical concepts (the practical syllogism and the enthymeme) allows for the surfacing of missing evidence and unstated directives underlying multimodal rhetoric. By stating multimedia arguments syllogistically, a technique is proposed for revealing hidden political sub-texts from beneath a putatively disinterested presentation of facts. The piece should be of interest to researchers, message designers and policy makers interested in the rhetoric of science, ecology, health and museums

Environmentalism, pre-environmentalism, and public policy

In the last decade, thousands of new grassroots groups have formed to oppose environmental pollution on the basis that it endangers their health. These groups have revitalized the environmental movement and enlarged its membership well beyond the middle class. Scientists, however, have been unable to corroborate these groups' claims that exposure to pollutants has caused their diseases. For policy analysts this situation appears to pose a choice between democracy and science. It needn't. Instead of evaluating the grassroots groups from the perspective of science, it is possible to evaluate science from the perspective of environmentalism. This paper argues that environmental epidemiology reflects ‘pre-environmentalist’ assumptions about nature and that new ideas about nature advanced by the environmental movement could change the way scientists collect and interpret data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45449/1/11077_2005_Article_BF01006494.pd