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.
By Abby Haynes on behalf of CIPHER (Centre for Informing Policy in Health with Evidence from Research)
When external providers deliver a complex program in an organisation, it is crucial that someone from that organisation—a liaison person—gives ‘insider’ advice and acts as a link between their organisation and the program providers. What are the characteristics to look for in filling that role? And how can liaison people best be supported?
Starting with richly articulated pictures of where we would like to be at some defined point in the future has powerful consequences for any human endeavour. How can we use such “Outcome Spaces” to guide the conception, design, implementation, and evaluation of transdisciplinary research?
Our Outcome Spaces Framework (Mitchell et al., 2017) considers three essential impacts:
(1) improving the situation,
(2) generating relevant stocks and flows of knowledge, and
(3) mutual and transformational learning by the researcher/s and involved participants.
What do researchers need to know about change to help our research have greater impact? What kind of impact is it realistic to expect? Will understanding change improve the ways we assess research impact?
The six lessons described here illustrate some of the complexities inherent in understanding and trying to influence change.
#1. Research findings enter a dynamic environment, where everything is changing all the time
As researchers we often operate as if the world is static, just waiting for our findings in order to decide where to head next. Instead, for research to have impact, researchers must negotiate a constantly changing environment.
La mayoría de los recientes enfoques para abordar problemas complejos no incluyen la dimensión política. Por otra parte, la ciencia política, así como los estudios de política pública y de gobierno contemporáneo han realizado escasas contribuciones al tratamiento de los procesos de toma de decisiones desde dinámicas complejas.
¿Cómo podemos desarrollar marcos innovadores que incorporen la dimensión política?
The language of ‘co-processes’ is much in vogue in policy, practice and academic communities worldwide. In commerce, product design and politics, the power of the crowd has long been recognised, but can co-processes be harnessed for the public good? The answer, right now, appears to be ‘maybe’.
What are co-processes and what are they for?
The briefest survey of the literature on co-processes confirms there is substantial variation in how they are defined and what methods or techniques they include. A confusing multiplicity of related terms exists—co-construction, co-production, co-design, co-innovation, co-creation—all are in regular use, sometimes interchangeably, and often defined at an unhelpful level of abstraction (for more on this topic see the blog post by Allison Metz on Co-creation, co-design, co-production, co-construction: same or different?). Nevertheless, however we define co-processes, participatory methods, boundary-spanning and inclusivity to varying degrees are foundational principles that can be detected in most accounts. Beyond that, the stated purposes and proposed outcomes vary considerably.
What are the results of participatory modeling efforts? What contextual factors, resources and processes contribute to these results? Answering such questions requires the systematic and ongoing evaluation of processes, outputs and outcomes. At present participatory modeling lacks a framework to guide such evaluation efforts. In this post I offer some initial thoughts on the features of this framework.
A first step in developing an evaluation framework for participatory modeling is to establish criteria for processes, outputs, and outcomes. Such criteria would answer a basic question about what it means when we say that a participatory modeling process, output, or outcome is good, worthy, or meritorious.
What kinds of change can implementation of research findings contribute to? Sometimes the aim is to make change happen, while at other times research implementation is in response to particular proposed or ongoing change.
Making change happen
Two ways of making change happen that are important for research impact are: 1) contributing to the on-going quest for improvement and 2) combatting practices or behaviours that have negative outcomes for individuals or society.