What knowledge is required by decision-makers and how is that determined?

Public engagement with science is fundamentally about the sharing of scientific knowledge with non-experts. In Chapter 2 I adapted the findings from the SPG (e.g. Cash et al., 2006; Walsh et al., 2014) to posit that knowledge needed to be accessible, credible, and relevant for decision-makers to actively engage and cognitively process it. Two new aspects of what knowledge decision-makers engage with emerged from the case studies. Before we consider if knowledge is accessible, credible or relevant we need also to consider how decisions are made regarding what issue requires engagement and, which source(s) of science will be used to address the issue. Who decides that an issue necessitates engagement is not unpacked systematically in either the PES or SPG discourses. In PES, campaigning publics are described who may demand dialogue around their issue of concern (Marres, 2005; Mohr et al., 2013) or governments initiate engagement on an issue deemed to be controversial in the public eye like genetically modified organisms (e.g. Rowe et al., 2005). For decision-makers the “science push/policy pull” dichotomy (Bielak et al., 2008) implies scientists “push” their own science and hope it is relevant to an issue while decision-makers, like campaigning publics, have an issue and decide what science they want to “pull”. The case studies indicate this dichotomy is an over-simplification.

AdaptNRM was part of a large complex funding package supporting new legislation around climate change and reducing carbon emissions (Figure 7). At first glance this appears to be a neat case of politically motivated policy pull. The issue prioritisation was clearly political but the identification of climate change as an issue associated with man-made carbon emissions was explicitly informed by science:

Why is Australia cutting its carbon pollution? The CSIRO, the Bureau of Meteorology, and Academies of Science from around the world have all advised that the world is warming and high levels of carbon pollution risk environmental and economic damage. (Commonwealth of Australia, 2012a, p. 4).

So an issue identified by scientific research was prioritised by the Australian Government for engagement. Scientific evidence also contributed to prioritizing issues addressed by NFEPA where scientific research had shown that over half of South Africa’s rivers and wetlands were considered threatened (Nel, Driver, & Swartz, 2011). AdaptNRM is also not a neat case of policy pull because the Federal government agency that sponsored and initiated engagement

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was not doing so for itself but for another tier of government entirely – regional NRM groups (Figure 7). The decision to target regional NRM groups as the audience for engagement rather than offering carbon credits and carbon offsets to farmers as previously mooted (Commonwealth of Australia, 2012a) is not publicly documented, but funder interviews indicated that it was in response to political pressure. Also, once the CSIRO won the tender to run AdaptNRM, the process arguably switched to science push (Figure 7) to the regional NRM groups who were expected to not only incorporate program outputs in their plans, but seemingly then engage with farmers to implement the new plans. So there are both push and pull elements in the AdaptNRM case and both scientific evidence and political imperatives shaped what issues were the subject of engagement in both the case studies. The push-pull dichotomy may be useful, therefore, for categorizing specific engagement activities but may obscure the more complex role of science communication in public decision-making. More broadly, for those interested in the role of science communication in policy development, my results suggest well-defined and funded activities such as the NFEPA and AdaptNRM projects may be snapshots in a more complex science communication landscape that exists for decision-makers.

The other key point here is how decisions are made around what science decision-makers are invited to engage with or the process of knowledge discovery. There are likely to be multiple possible technical approaches to most NRM issues but in both cases, experts were primarily selected from the lead science institute and established partners, seemingly for pragmatic reasons (they were known and available). In South Africa, the NFEPA project team chose a conservation planning approach but other approaches to prioritising areas for conservation exist. Also, for a given approach there are likely different research groups working in that field who could contribute. For example, Australia has a lot of expertise in freshwater conservation that may have been applicable to NFEPA but Australian scientists were not involved in the project. In AdaptNRM, scientists within the project team decided to employ science and scientists primarily from within CSIRO to address the topics NRM groups had identified as of greatest interest in relation to climate change adaptation. It is arguable that geographically specific expertise from local scientists was critical in both cases and was certainly valued by decision-makers, and accessing local expertise is likely easier and cheaper. The point, however, is that decisions about knowledge sources and approaches were not well documented or transparent (although potentially discussed with participants).

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Why might it be preferable to have transparent processes for determining which approach to an issue to adopt and which science or scientists to involve? Transparent decision-making processes have been identified as a hallmark of good engagement because—along with fairness, independence, representativeness and early involvement—they foster legitimacy which is seen as the foundation for public acceptance (Rowe et al., 2004). While there is no direct evidence that opaque knowledge selection processes had negative consequences in either case, some AdaptNRM participants were aware of other relevant science that wasn’t included but were unclear as to why. Without documentation or explanation for knowledge exclusion, the legitimacy of the process may be compromised. Beyond documentation, involving participants in the knowledge selection process may lead to discovery and elicitation of new sources (as was evident to a degree in both cases) and may “open up” ideas as to what relevant knowledge may be (Stirling, 2008). For this reason, I suggest having transparent and open knowledge selection in decision-maker engagement increases the legitimacy of engagement processes. In the SPG it has been noted that for research to be considered legitimate by both scientists and decision-makers it needs to have been “developed via a process that considers the values and perspectives of all actors” (Cook et al., 2013, p. 671). I would argue however, that NRM decision-makers are often poorly placed to judge whether the best or most appropriate science has been selected for discussion. These decisions may rightly be the domain of the scientists but there is currently little incentive for transparency or reflexivity on behalf of scientists or engagement teams in making decisions about what approaches and groups of scientists to include in decision-maker engagement, in Australia at least.

Another benefit of transparent and legitimate processes may be that decisions arising from such processes are more likely to be accepted, even by people who may not agree with them (Russell, 2013). Decision-makers in both cases placed a great deal of importance on the acceptance of their decisions by key stakeholders. So instead of the idea of using science in decisions to create legitimacy, my suggestion is legitimacy is created by open consultative processes of knowledge identification and use (subject to available resources and other constraints). Importantly, open transparent knowledge selection processes may also identify or elicit important knowledge unknown at the start of the process which may ultimately enable better decisions. Indeed this knowledge discovery or elicitation was a key value of both AdaptNRM and NFEPA.

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Findings from the case studies indicate that when we evaluate engagement exercises we need to think about not only what scientific knowledge has been included but what other types of knowledge can contribute or are required for robust, decision-making. Both cases showed that incorporating science into public decision-making (particularly in complex contexts requiring multiple trade-offs) is not as simple as cutting and pasting science into a plan. The “non-scientific” knowledge that was critical in both cases was that of public decision-makers and private consultants. More specifically, this consisted of knowledge about the organisations and institutions that develop and implement policy (e.g. what mechanisms for action exist); knowledge of the decision context (e.g. the nature of stakeholders); and knowledge about the application of science. In addition, the knowledge and expertise of the project teams managing and facilitating the processes (predominantly CSIRO in Australia, CSIR and SANBI in South Africa) was also valued by decision-makers and key to good process and outcomes. While the cases illustrate the value of decision-maker knowledge, other possible sources of knowledge that may be useful and necessary include local knowledge, indigenous knowledge, and business, political and professional understandings (Head, 2013; Tengö et al., 2014).