SMART social science? Examining the nature and role of social scientific expertise in institutional design

Daniel Bromley argues against Oran Young’s FIT model as a basis for environmental governance, on the grounds that humans cannot manage nature and that attempts to do so are based on a scientistic, modernist conceit. At issue is the role of natural and social scientists in adjudicating questions about what we ought to do to close governance gaps and address unsustainable behaviors. If Bromley is right, then the lessons of the American pragmatist tradition recommend against attempts to “fit” social institutions to the natural world. The first objective of this paper is to argue that Bromley’s view is not in keeping with the pragmatism of C. S. Peirce and John Dewey, which actually places a high value on natural and social scientific modes of inquiry in the service of social ends. I argue that Young’s proposal is in fact a development of the pragmatist idea that social institutions must be fit in the sense of fitness, i.e., resilient and able to navigate uncertainty. Social institutions must also evolve to accommodate the emerging values of the agents who operate within them. The second objective of this paper is to examine the role of social science expertise in the design of social policies. Governance institutions typically rely on the testimony of natural scientists, at least in part, to understand the natural systems they operate within. However, natural systems are also social systems, so it seems pertinent to ask whether there is a role for social systems experts to play in helping to design environmental governance institutions. I argue that social scientists can make a unique contribution as experts on social institutions, and as such, are necessary to bring about a transformation of the unsustainable institutions that are preventing us from achieving stated sustainable development goals.

Exploring social representations of adapting to climate change using topic modeling and Bayesian networks

When something unfamiliar emerges or when something familiar does something unexpected people need to make sense of what is emerging or going on in order to act. Social representations theory suggests how individuals and society make sense of the unfamiliar and hence how the resultant social representations (SRs) cognitively, emotionally, and actively orient people and enable communication. SRs are social constructions that emerge through individual and collective engagement with media and with everyday conversations among people. Recent developments in text analysis techniques, and in particular topic modeling, provide a potentially powerful analytical method to examine the structure and content of SRs using large samples of narrative or text. In this paper I describe the methods and results of applying topic modeling to 660 micronarratives collected from Australian academics / researchers, government employees, and members of the public in 2010-2011. The narrative fragments focused on adaptation to climate change (CC) and hence provide an example of Australian society making sense of an emerging and conflict ridden phenomena. The results of the topic modeling reflect elements of SRs of adaptation to CC that are consistent with findings in the literature as well as being reasonably robust predictors of classes of action in response to CC. Bayesian Network (BN) modeling was used to identify relationships among the topics (SR elements) and in particular to identify relationships among topics, sentiment, and action. Finally the resulting model and topic modeling results are used to highlight differences in the salience of SR elements among social groups. The approach of linking topic modeling and BN modeling offers a new and encouraging approach to analysis for ongoing research on SRs.