Processes to support the uptake of research evidence call for each of the key stakeholders to consider the challenges faced by other key stakeholders in making good use of research evidence. When stakeholders have the opportunity to consider perspectives other than their own, they will generally have a broader understanding of the problem space, and, in turn a greater commitment to co-creating prototypes for improving research translation.
Let’s consider a real world example in New York City’s public child welfare system.
By Flurina Schneider, Lara M. Lundsgaard-Hansen, Thoumthone Vongvisouk, and Julie G. Zähringer
How can science truly support sustainability transformations?
In our research projects we often find that the very process of co-producing knowledge with stakeholders has transformative impacts. This requires careful design and implementation. Knowledge co-production in transdisciplinary and other research leads to social learning and can make a difference in the lives of those involved.
Can we help the next generation of policy makers, business leaders and citizens to become creative, critical and independent thinkers? Can we make them aware of the nature of the problems they will be confronted with? Can we strengthen their capacity to foster and lead stakeholder processes to address these problems?
It seems simple enough to say that community values and aspirations should be central to informing government decisions that affect them. But simple things can turn out to be complex.
In particular, when research to inform land and water policy was guided by what the community valued and aspired to rather than solely technical considerations, a much broader array of desirable outcomes was considered and the limitations of what science can measure and predict were usefully exposed.
How can we improve the often poor interaction and lack of genuine discussions between policy makers, experts, and those affected by policy?
As a social scientist who makes and uses models, an idea from Daniel Dennett’s (2013) book ‘Intuition Pumps and Other Tools for Thinking’ struck a chord with me. Dennett introduces the idea of using lay audiences to aid and improve understanding between experts. Dennett suggests that including lay audiences (which he calls ‘curious nonexperts’) in discussions can entice experts to err on the side of over-explaining their thoughts and positions. When experts are talking only to other experts, Dennett suggests they under-explain, not wanting to insult others or look stupid by going over basic assumptions. This means they can fail to identify areas of disagreement, or to reach consensus, understanding, or conclusions that may be constructive.
For Dennett, the ‘curious nonexperts’ are undergraduate philosophy students, to be included in debates between professors. For me, the book sparked the idea that models could be ‘curious nonexperts’ in policy debates and processes. I prefer and use the term ‘interested amateurs’ over ‘curious nonexperts’, simply because the word ‘amateur’ seems slightly more insulting towards models!
I am a firm believer in looking at interdisciplinary collaboration and knowledge exchange – or impact generation – as processes. If you can see something as a process, you can learn about it. If you can learn about it, you can do it better!
I find that this approach helps people to feel enfranchised, to believe that it is possible for them to open up what might have seemed to be a static black box and achieve understanding of the dynamics of how nouns like ‘interdisciplinarity’ or ‘knowledge exchange’ or ‘research impact’ can actually come to be.
How can non-indigenous researchers work with indigenous communities to tackle complex socio-ecological issues in a way that is culturally appropriate and does not contribute to the marginalisation of indigenous interests and values?
These questions have long been considered by participatory action researchers, and are of growing relevance to mainstream science organisations, which are increasingly utilising cross-cultural research practices in recognition of the need to move beyond identifying ‘problems’ to finding ‘solutions’.
As an example, I borrow heavily from work with colleagues in a partnership involving the Institute of Environmental Science and Research (a government science institute), Hokianga Health Enterprise Trust (a local community owned health service) and the Hokianga community.
Storytelling ethnography is a valuable tool if your research traverses several disciplines and aims for insights that transcend all of them. Stories not only integrate knowledge from diverse disciplines, but can also “change the way people act, the way they use available knowledge” (Griffiths 2007).
The special qualities of transdisciplinarity are:
its potential for integrative inquiry and emergent solutions,
its engagement with community and other non-academic knowledges, and
the breadth of its outcomes for researchers, participants and the wider community.
These are also qualities of what I call storytelling ethnography.
How can co-creation communities use models – simple visual representations and/or sophisticated computer simulations – in ways that promote learning and improvement? Modeling techniques can serve to generate insights and correct misunderstandings. Are they equally as useful for fostering new learning and adaptation? Sterman (2006) argues that if new learning is to occur in complex systems then models must be subjected to testing. Model testing must, in turn, yield evidence that not only guides decision-making within the current model, but also feeds back evidence to improve existing models so that subsequent decisions can be based on new learning.
How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?
Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’
How does the mismatch between policy and research processes and timelines stymie co-creation? I describe an example from a project in Sachsen-Anhalt state in Germany, along with lessons learnt.
The project, initiated by researchers, aimed to use a more participatory approach to developing agri-environmental schemes, in order to improve their effectiveness. Officers from the Agricultural Payments department of the Sachsen-Anhalt Ministry for Agriculture were invited to participate in an action research project that was originally conceived to also involve officers from the Conservation department of the same ministry, farmer representatives and conservation groups.
¿Cómo pueden los gobiernos, las comunidades y el sector privado efectivamente trabajar juntos para lograr un cambio social hacia el desarrollo sostenible?
En este blog describo los procesos claves que permitieron a Uruguay lograr uno de los regímenes más avanzados de protección del suelo de tierras de cultivo de secano en el mundo. Una explicación del proceso es la creación de una cultura pragmática de la complejidad, una cultura inclusiva, deliberativa que reconoce la naturaleza compleja del problema y abraza el potencial de lo posible.