By IIASA Science Writer and Editor Daisy Brickhill.
“The thing about communicating science today is…people can always watch cat videos instead. And let’s face it, some of those clips are really funny.”
Marshall Shepherd, former president of the American Meteorological Society, smiles at the audience of this science communication seminar, aware of the frustrated sighs going on in the room, and in some cases the blank incredulity—people wouldn’t watch cat videos when they could be paying attention to my science, surely?
We are at the AAAS annual meeting, a vast conference with around 10,000 attendees from all walks of life, from toddlers to retired professors. The science presented here is truly diverse, and covers everything from radically successful new cancer treatments, to advances in artificial intelligence, to the IIASA session on how we can hope to achieve all 17 UN Sustainable Development Goals.
Shepherd is speaking of his long career engaging with the public about his work on weather systems and climate change. “Get out of your ivory tower,” he urges all researchers. There are important issues at stake, and what if no one speaks for the scientific evidence?
However, communicating science effectively is not easy. Understanding something does not mean you are automatically good at explaining it. All through academic training researchers learn how to speak to people in their own field, who talk just like them. That’s important, they might be your next reviewer, after all. But it is only one, narrow form; engaging the public requires a high level of understanding, not just of the topic, but of the audience and communication itself.
“We have left behind the old idea of science communication where brains are empty vessels waiting to be filled,” says the next speaker, Barbara Klein Pope, executive director for communications for the National Academies. “They are a swamp, and we need to explore that swamp to communicate properly.” She describes research which tested the effects of different types of communication on people’s perceptions of social science, in terms of whether they felt it was worth funding, for instance (oh yes, I sense the academic ears pricking up now).
The findings of this work led to a framework of three clear messages. First, use exemplars—a good example can do wonders—yes, your research might be relevant across reams of different cases but general, expansive terms are often vague and a simple example can bring clarity.
Second, the all-important yet surprisingly often neglected question, “Why do we care?” Bear in mind also that it’s not why you care, you’ve made a career out of this science, we know why you care, but why should your audience care.
Finally, use metaphors. Science is often very complex, and pretty much anyone outside your field will need something they can relate to—a familiar concept that they can use to begin to explore the new territory. In case you need more convincing, the use of metaphors was shown to have a significant effect on whether the public felt the work was worth funding.
At the end of that session I was struck by the parallels between this session and another I attended on science-policy interactions with speakers Vladimír Sucha, Daniel Sarewitz, and Peter Gluckman, all working at the forefront of science-policy.
Trust, built on good communication, is vital, the speakers all agreed. Interesting conclusions should not be buried at the end of a report, they should be at the start, just as they would be for the public, and any article or briefing should be kept as short and relevant as possible. Examples and metaphors play a role here too, and a good story with persuasive anecdotes can have much more impact than a dry report.
What not to do, Sucha reminds us, is send an email saying “Here are the links to 200 peer-reviewed papers on this, you’ll find it all there.” After all, policymakers can access cat videos just as easily as the rest of us.
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.