Productive multivocal analysis – Part 2: Achieving epistemological engagement

Community member post by Kristine Lund

kristine-lund
Kristine Lund (biography)

In a previous blog post I described multivocalityie., harnessing multiple voices – in interdisciplinary research and how research I was involved in (Suthers et al., 2013) highlighted pitfalls to be avoided. This blog post examines four ways in which epistemological engagement can be achieved. Two of these are positive and two may have both positive and negative aspects, depending on how the collaboration plays out.

Once a team begins analyzing a shared corpus from different perspectives — in our case, it was a corpus of people solving problems together — it’s the comparison of researchers’ respective analyses that can be a motor for productive epistemological encounters between the researchers. Continue reading

Productive multivocal analysis – Part 1: Avoiding the pitfalls of interdisciplinarity

Community member post by Kristine Lund

kristine-lund
Kristine Lund (biography)

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? Continue reading

Toolkits for transdisciplinary research

Community member post by Gabriele Bammer

gabriele-bammer
Gabriele Bammer (biography)

If you want to undertake transdisciplinary research, where can you find relevant concepts and methods? Are there compilations or toolkits that are helpful?

I’ve identified eight relevant toolkits, which are described briefly below and in more detail in the journal GAIA’s Toolkits for Transdisciplinarity series.

One toolkit provides concepts and methods relevant to the full range of transdisciplinary research, while the others cover four key aspects: (i) collaboration, (ii) synthesis of knowledge from relevant disciplines and stakeholders, (iii) thinking systemically, and (iv) making change happen. Continue reading

Bringing the Immunity-to-Change™ process to the scientific community

Community member post by Erica Lawlor and Cheryl Vaughan

erica-lawlor
Erica Lawlor (biography)

How can scientists whose careers were formed in an incentive system that cultivates competitive and territorial behaviors be helped to meet the expectations of collaborative research frameworks? A team-based approach that transcends disciplinary boundaries may be a tall order for scientists who “grew up” in a system where funding and promotion are based upon a proven record of individual contributions to a field of research. But that is the direction in which much of science is heading. Continue reading

Team science glossary

Community member post by Sawsan Khuri and Stefan Wuchty

stefan-wuchty
Stefan Wuchty (biography)
sawsan-khuri
Sawsan Khuri (biography)

As team science gains momentum, we present this glossary to standardize definitions for the most frequently used terms and phrases in the science of team science literature, and to serve as a reference point for newcomers to the field. Source material is provided where possible. Continue reading

Research team performance

Community member post by Jennifer E. Cross and Hannah Love

jennifer-cross
Jennifer E. Cross (biography)

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:

  1. group membership,
  2. rules of engagement, and
  3. patterns of interaction.

Each of these three predictors can be influenced in order to improve the performance of teams, as the following examples show. Continue reading