Many voices are expressed when researchers from different backgrounds come together to work on a new project and it may sound like cacophony. All those voices are competing to be heard. In addition, researchers make different assumptions about people and data and if these assumptions are not brought to light, the project can reach an impasse later on and much time can be wasted.
So how can such multivocality be positive and productive, while avoiding trouble? How can multiple voices be harnessed to not only achieve the project’s goals, but also to make scientific progress?
How can understanding philosophy improve our research? How can an understanding of what frames our research influence our choices? Do researchers’ personal thoughts and beliefs shape research design, outcomes and interpretation?
These questions are all important for social science research. Here we present a philosophical guide for scientists to assist in the production of effective social science (adapted from Moon and Blackman, 2014).
How can we improve the creativity and performance of research teams?
Recent studies on team performance have pointed out that the performance and creativity of teams has more to do with the social processes of interaction on teams, than on individual personality traits. Research on creativity and innovation in teams has found that there are three key predictors of team success:
group membership,
rules of engagement, and
patterns of interaction.
Each of these three predictors can be influenced in order to improve the performance of teams, as the following examples show.
How can collaborative groups move past their divisions and find solutions that advance their shared notions of what would be good for the community?
Complex problems – such as how to expand access to high-quality health care, how to reduce poverty, how to remedy racial disparities in educational attainment and economic opportunity, and how to promote economic development while at the same time protecting natural resources – can’t be solved with technical remedies or within a narrow mindset. They require the sort of multi-disciplinary, nuanced analysis that can only be achieved by engaging a variety of stakeholders in a co-creative process.
Bringing together stakeholders with diverse perspectives allows for a comprehensive analysis of complex problems, but this also raises the risk of a divisive process.
After one year or so delivering seminars that share practical techniques to help navigate complexity to public sector audiences, I’ve observed two simple and fundamental barriers to dealing more effectively with complex, interdisciplinary problems in the public sector.
First, is the lack of time to problem-solve – to pause and reflect on an issue, to build a deeper understanding of it, to think creatively about it from different angles, to think through some ideas, to test out some ideas. There is too much else going on.
Second, is that it’s often quite difficult to put one’s collective finger on what, exactly, the problem is.
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.
Consider the real-world case I was involved in of a meeting in a school district that intends to roll-out a new mathematics curriculum and support teachers’ use of the new curriculum through professional development. The district has made a large monetary investment in the curriculum and professional development both through the purchase of materials and the dedication of human resources to the effort.
What are the ingredients of successful research collaboration? How can we make collaboration work when we are all getting busier?
One of the best guides to success in collaborative team work was produced by Michelle Bennett, Howard Gadlin and Samantha Levine-Findlay in 2010. Built on the experience of researchers at the US National Institutes of Health, they explored: preparing for collaboration, selecting team members, fostering trust, sharing credit, handling conflict and more.
An additional way of thinking about collaboration that I have found useful (Bammer 2008) is to consider it as a process of harnessing and managing differences.
Would a focus on ‘knowledge ecology’ provide a useful alternative to ‘knowledge integration’ in inter- and trans-disciplinary research?
My experience in bringing perspectives from the humanities, arts and social sciences (HASS) to projects led by researchers from science, technology, engineering and mathematics (STEM) has led me to agree with Sharp and colleagues (2011) that ‘knowledge integration’ is essentially a positivist concept, dependent on the idealist model of a unified field of scientific knowledge to which every bit of science contributed.
Many partners and co-researchers from STEM backgrounds, it seems, cannot recognise other knowledge paradigms and can only ‘integrate’ knowledge in the form of quantitative data. HASS research is excluded or disqualified as merely ‘anecdotal’ or ‘subjective’. Like racial or cultural assimilation, knowledge integration seems to require non-dominant knowledges to disguise or erase their unique differentiating features in order to blend with the dominant positivist paradigm.
What does it mean to include ‘a social scientist’ in a team tackling complex problems? Here I focus on complex environmental problems and how biophysical and social scientists work together. I’m curious if social scientists face the same issues in other problem areas, such as health.
Things have improved since my early academic career, when I was often asked to justify why a social scientist deserved a seat at the table when discussing environmental questions. It seemed that even supportive natural scientists were motivated to engage their social science colleagues only to ‘fix’ some type of problem caused by people (e.g., politicians, decision-makers, managers, the “general public”).
While it’s now normal for social scientists to be included, they tend to be lumped together, unlike the biophysical scientists who are differentiated into a range of disciplines with relevant specialization areas.
What characterizes multidisciplinary research? When is it most appropriate? What does it take to do it well? Multidisciplinarity often gets a bad rap, being seen as less sophisticated than interdisciplinarity and transdisciplinarity. But does it have its own important role in dealing with complex social and environmental problems?
Multidisciplinary research has two primary characteristics:
different disciplines independently shine their light on a particular problem, and
synthesis happens at the end and can be undertaken by anyone.
Unlike interdisciplinary and transdisciplinary research, there is no attempt to agree upfront on either a problem definition or on how the different perspectives will be brought together.
Como conseguir que um grupo multidisciplinar integrado por economistas, climatologistas, geógrafos, antropólogos, biólogos, sociólogos, jornalistas, engenheiros químicos, engenheiros ambientais e advogados trabalhe de maneira mais interdisciplinar?
Esse foi o desafio encarado por um projeto de pesquisa sobre as percepções de agricultores familiares de quatro biomas brasileiros (a Amazônia, o Cerrado, o Pantanal e o Semiárido) sobre os impactos que as mudanças climáticas estão tendo nos seus modos de vida. Esse pequenos produtores, com baixa disponibilidade de capital, estão expostos a riscos naturais e socioeconômicos, e são extremadamente vulneráveis aos eventos climáticos extremos.
Um fator chave foi a demarcação do marco teórico do projeto, que incluiu a hipótese de que o sucesso das politicas de adaptação aumenta consideravelmente quando essas políticas se baseiam em um conhecimento de primeira mão das realidades cotidianas e das percepções das populações envolvidas.
O desenho da pesquisa foi guiado por três elementos básicos:
How can we address social, environmental, political and health problems that are too big and too complex for any single person, organization or institution to solve, or even to budge? How can we pool our wisdom and work collaboratively toward purposes that are larger than ourselves?
In theory at least, co-creation generates innovative solutions that transcend what would otherwise be produced by the participants acting on their own. In other words, co-creation can foster synergy.
To maximize synergy, a co-creative group should include participants who understand the problem from all the relevant perspectives. The more complex the problem, the greater the number and diversity of stakeholders who should be included in the process. A broader range of perspectives and ways of thinking allows for a richer and more comprehensive analysis of the problem, as well as more innovative solutions that address more of the underlying factors.
