An approach to consider the impact of co-designed science: Case study of Bayesian networks to set NRM targets

It is now often stated that the scientific community can gain greater impact from their work if they engage intended stakeholders in co-design, implementation and evaluation of projects. However, the evidence to substantiate these claims are rarely more than anecdotal and reflective comments from scientists. Previous evaluation methods for participatory work run the risk of being over simplistic, and therefore omit important impacts, or become too complex and not user-friendly. In this paper, we provide an intermediate option, combining the research of others (detailed in Table 1), to produce 5 key dimensions to consider when evaluating the impact of participatory projects. These dimensions are 1) an increase in knowledge and awareness, 2) a change in behavior or practice, 3) active dissemination of new knowledge, 4) change in policy or planning, or some level of government endorsement, and 5) physical system improvements or “on-ground” change. In addition, social learning, empowerment and new social norms are important, but due to complexity, they are not yet included in this method. Each dimension can be estimated for its breadth and depth of the impact by more detailed criteria (e.g. how many people have increased their knowledge? And how much more do they know (i.e. could they explain it to someone else?)). The breadth is more of a quantitative assessment, which is generally easier to measure, while the depth, particularly if self-assessed, is more likely to be qualitative and subject to bias. We provide a grid to plot the breadth and depth impacts, and the means to combine this impact into a single visual representation on a radar plot (Figure 1). Here multiple lines represent different people's views of the same project, but they could also show the impact of different projects, or both.