By Lorne A. Whitehead, Scott H. Slovic and Janet E. Nelson
How can we recognize and encourage investigations that holistically fuse fundamental and applied research on a problem of interest in a manner that is both (a) integrative and recursive and (b) highly collaborative with non-university experts?
We refer to this form of research as “Highly Integrative Basic And Responsive” (HIBAR). It adds deep university-society engagement to the work that Donald Stokes named “Pasteur’s quadrant” (Stokes 1997) and others have called “use-inspired basic research”.
How can practical mapping help develop interdisciplinary knowledge for tackling real-world problems — such as poverty, justice and health — that have many causes? How can it help take into account political, economic, technological and other factors that can worsen or improve the issues?
Maps are useful because they show your surroundings – where things are in relation to each other (and to you). They show the goals we want to achieve and what it takes to get there.
‘Practical mapping’ is a straight-forward approach for using concepts and connections to integrate knowledge across and between disciplines, to support effective action.
What are some of the key frameworks that can be used for transdisciplinary research? What are their particular strengths? How can you choose one that’s most suitable for your transdisciplinary project?
The nine frameworks described here were highlighted in a series for which I was the commissioning editor. The series was published in the scientific journal GAIA: Ecological Perspectives in Science and Society between mid-2017 and end-2019.
Choosing among them is not a matter of right or wrong, but of each being more or less helpful for a particular problem in a particular context.
What is expertise in research integration and implementation? What is its role in helping tackle complex societal and environmental problems, especially those dimensions that define complexity?
Expertise in research integration and implementation
Addressing complex societal and environmental problems requires specific expertise over and above that contributed by existing disciplines, but there is little formal recognition of what that expertise is or reward for contributing it to a research team’s efforts. In brief, such expertise includes the ability to:
identify relevant disciplinary and stakeholder inputs
effectively integrate them for a more comprehensive understanding of the problem
support more effective actions to ameliorate the problem.
By Steven Lam, Michelle Thompson, Kathleen Johnson, Cameron Fioret and Sarah Hargreaves
How can graduate students work productively with each other and community partners? Many researchers and practitioners are engaging in transdisciplinarity, yet there is surprisingly little critical reflection about the processes and outcomes of transdisciplinarity, particularly from the perspectives of graduate students and community partners who are increasingly involved.
Our group of four graduate students from the University of Guelph and one community partner from the Ecological Farmers Association of Ontario, reflect on our experiences of working together toward community food security in Canada, especially producing a guidebook for farmer-led research (Fioret et al. 2018). As none of us had previously worked together, nor shared any disciplines in common, we found it essential to first develop a guiding framework for collaboration. Our thinking combined the following key principles from action research and transdisciplinarity:
What do you think about mathematical modelling of ‘wicked’ or complex problems? Formal modelling, such as mathematical modelling or computational modelling, is sometimes seen as reductionist, prescriptive and misleading. Whether it actually is depends on why and how modelling is used.
Here I explore four main reasons for modelling, drawing on the work of Brugnach et al. (2008):
The most familiar models are predictive, such as those used to forecast the weather or plan the economy. However, models have many different uses and different modelling techniques are more or less suitable for specific purposes.
Here I present an example of how a game and a computerised agent-based model have been used for knowledge synthesis and decision support.
The game and model were developed by a team from the Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), a French agricultural research organisation with an international development focus. The issue of interest was land use conflict between crop and cattle farming in the Gnith community in Senegal (D’Aquino et al. 2003).
Agent-based modelling is particularly effective where understanding is more important than prediction. This is because agent-based models can represent the real world in a very natural way, making them more accessible than some other types of models.
What is the groan zone in collaboration? What can you do when you reach that point?
As researchers and practitioners engaged in transdisciplinary problem-solving, we know the value of diverse perspectives. We also know how common it is for groups to run into challenges when trying to learn from diverse ideas and come to consensus on creative solutions.
This challenging, often uncomfortable space, is called the groan zone. The term comes from Sam Kaner’s diamond model of participation shown in the figure below. After an initial period of divergent thinking, where diverse ideas are introduced, groups have to organize that information, focus on what’s most important, and make decisions in order to move forward into the phase of convergent thinking.
Where does the term incommensurability come from? What is its relevance to interdisciplinarity? Is it more than plain difference? Does incommensurability need to be reconceptualized for interdisciplinarity?
Incommensurability: its origins and relevance to interdisciplinarity
‘Incommensurability’ is a term that philosophers of science have borrowed from mathematics. Two mathematical magnitudes are said to be incommensurable if their ratio cannot be expressed by a number which is an integer. For example, the radius and the circumference of a circle are incommensurable because their ratio is expressed by the irrational number π.
What’s needed to enable the integration of concepts, theories, methods, and results across disciplines? Why is communication among experts important, but not sufficient? Interdisciplinary experts must also meta-cognize: both individually and as a team they must monitor, evaluate and regulate their cognitive processes and mental representations. Without this, expertise will function suboptimally both for individuals and teams. Metacognition is not an easy task, though, and deserves more attention in both training and collaboration processes than it usually gets. Why is metacognition so challenging and how can it be facilitated?
Interdisciplinary collaboration to tackle complex problems is challenging! In particular, interdisciplinary communication can be very difficult – how do we bridge the gulf of mutual incomprehension when we are working with people who think and talk so very differently from us? What skills are required when mutual incomprehension escalates into conflict, or thwarts decision making on important issues?
It is often at this point that collaborations lose momentum. In the absence of constructive or productive exchange, working relationships stagnate and people retreat to the places where they feel safest:
How do you write-up the methods section for research synthesizing knowledge from different disciplines and stakeholders to improve understanding about a complex societal or environmental problem?
In research on complex real-world problems, the methods section is often incomplete. An agreed protocol is needed to ensure systematic recording of what was undertaken. Here I use a checklist to provide a first pass at developing such a protocol specifically addressing how knowledge from a range of disciplines and stakeholders is brought together.
KNOWLEDGE SYNTHESIS CHECKLIST
1. What did the synthesis of disciplinary and stakeholder knowledge aim to achieve, which knowledge was included and how were decisions made?