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The RIU model as an analytical framework for scientific knowledge transfer: the case of the “decision support system forest and climate change”

Since a direct, so-called “linear” scientific knowledge transfer from science to political practice does not seem to be possible, we suggest an alternative model of the science–policy-interface. This model understands scientific knowledge transfer as the connection between research (R), integration (I), and utilization (U)—the RIU model. Within this RIU-model, scientific knowledge is produced in the science system (research), and science-based problem solutions are utilized within practice by political actors (utilization). Between the two spheres there is no “automatic” connection that leads to a linear application of science in policy making. Rather, the RIU-model highlights the important sphere of “integration”, a step that lies between science and utilization. A case study on a German decision support system for sustainable forest management within climate change is presented. It is shown that this informational instrument failed since no application in practice could be observed. An analysis by using the RIU model demonstrates (1) what are the reasons for the failure in this case and (2) which recommendations can be drawn by the RIU model for scientific advice that matters in forest policy-making.
- University of Göttingen Germany
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).44 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
