Floods & Forest Fires: Understanding the (mis)Alignment of Research & Needs

Sunday, 15 February 2015
Exhibit Hall (San Jose Convention Center)
Eric B. Kennedy, Consortium for Science, Policy, and Outcomes; Decision Center for a Desert City, Arizona State University, Tempe, AZ
[Background] Over the past several decades, the science and policy communities have begun to shift away conducting and supporting ‘research for research’s sake’ alone. Rather, there is emerging agreement on the need to have ‘well ordered’ (Kitcher, 2003) or ‘well aligned’ science (Sarewitz & Pielke, 2007) – research that is of interest, relevance, and use to real-world practice. Yet, such a movement raises key questions: How do you actually know when work is relevant or useful? How well aligned is existing research? And, how might we improve this alignment? [Methods] To further abstract theorizing on the concept of well-aligned science, we performed an ethnographic observation of a meeting between water & forest managers of the American Southwest. This meeting was an ideal case study for two reasons: First, its explicit mission to identify existing research and current gaps provided ample material for analysis. Second, it brought together both practitioners and academics from two communities (forestry and water management) who have only recently begun collaborating on their intersecting responsibilities. Two ethnographers documented and coded the full two days of conversations. [Results] These observations were categorized on several dimensions, including the (a) types of gaps identified, (b) occurrences of existing research vs. open gaps, and (c) types of research required to meet practitioner needs. The resulting clusters were mapped onto existing frameworks of uncertainty (e.g., White et al., forthcoming) and misaligned science (e.g., Sarewitz & Pielke, 2007) to test whether these approaches captured the actual examples of misalignment. [Conclusions] While the results document real-world cases of misalignment between the needs of practitioners and what scientists have thus far provided, they also demonstrate that these frameworks are inadequate to characterize – let alone anticipate and proactively address – misalignment. We develop an alternative framework for understanding types of misalignment and uncertainty.