Working on a system analysis of PUS: developing complementary data streams
Social attitudes to science must also be understood in the context of very iconic imagination, which comes to light when common sense is challenged by scientific research. Representations familiarise the unfamiliar and give attitudes the direction of approach or avoidance (Farr 1993; Wagner 2007), and these are widely’ cultivated in public discourse. Such an outlook shifts PUS research from rank-ordering people by gradients to characterising their stereotypes in everyday life, often centred on medicine and health concerns (e.g., Boy 2012; Bauer and Schoon 1993; Durant et al. 1992). In the twenty-first century, the making of science is global, but science culture is local and likely to remain so. Studying these representations of science opens the door widely for mixed methods and comparative enquiries into the cultivation of images of science (Bauer and Gaskell 2008; Wagner and Hayes 2005).
PUS research thus extends its range of data streams. Media monitoring, in print and recently social media, is ever more cost-effective, easily extended backwards in time or monitored in real-time and digital. Beyond any immediate moral panic over ‘fake news’, this is an enormous field of cultural-symbolic activity. Mapping the salience and framing of science offers mid- and long-term indicators of attention and content trends, such as the médicalisation of news (Bauer 1998), of issue cycles (Schafer 2012), and of long waves of attention (Falade et al. 2019; Bauer 2012a; Bauer et al. 2006; Bucchi and Mazzolini 2003; LaFollette 1990). Social media such as Twitter, Reddit and blogs display fast-moving targets of attention, topic flow and public sentiments about science that can be captured with new text-analytic techniques including machine learning algorithms. The more specific the topic, the easier is selection (scraping), corpus construction (parsing, annotation) and analysis (topic modelling); that leaves the characterisation of a general theme such as ‘science’ a desirable target among the proliferation of specific topics (see chapters by Suerdem or Neresini in Bauer et al. 2019). Public attention to science is no historical constant, and these fluctuations are far from understood.
A system analysis also recruits enquiries into the mobilisation of scientists (Bauer and Jensen 2011; Entradas and Bauer 2017). Mobilising scientists into public engagement activities invites comparison to the great awakenings in American evangelism (Barkun 1985); these are cyclical events rather than historical constants. To get a better sense of these efforts, we need investigations of the latest wave of mobilisation of scientists since the 1980s, in different places at different times. The very existence of PUS surveys is itself indicative of this effort in a historical context that deserves to be elucidated (see above Tables 14a, 14b, 14c, and Gregory’ and Lock 2008).
In conclusion, we have reviewed three paradigms of asking questions about the public understanding of science — literacy, public attitudes, and science in-and-of society — and listed most of the national survey efforts which addressed such questions over the past 70 years. This growing global database enables researchers through a number of avenues to ask: what has changed with regard to levels of literacy, the range of public attitudes, and science and society relations, and are these changes a feature of diversity in ‘science cultures’ across the globe? In all this, the ‘public deficit model’ proves to be a very special case. The relationship between knowledge of and attitudes to science depends on the context, namely the level of socio-economic development and the debates and controversies in society that come with it.
