Ten steps to make your research more relevant

Community member post by Christian Pohl, Pius Krütli and Michael Stauffacher

Interdisciplinary and transdisciplinary research often aims at broader impact in society. But, how can you make such impact happen?

A researcher might face a number of questions (s)he was not necessarily trained to address, such as:

  • How can I be sure that my research question will provide knowledge relevant for society?
  • Who in this fuzzy thing called society are my primary target audiences anyway?
  • Are some of them more important for my project than others?

Over the last several years, we developed 10 steps to provide a structured way of thinking through how to improve the societal relevance of a research project, as summarised in the table below.

When working with researchers to plan their impact, we usually go through the 10 steps in a workshop format, as follows:

  • Before each step we provide a brief account of the underlying theory and clarify why the step matters.
  • Then we ask the researchers to complete a concrete task, reflecting on their own project
  • Researchers usually also discuss their reflections with each other and learn about different approaches to address societal relevance.
  • They also discuss the tasks with us, but we are not necessarily the ones who know the right answers.

The ten steps work best in a context where a research project leader, for example, provides detailed project knowledge and the whole group is interested in discussing the societal impact of research.

In our experience, the ten steps trigger reflection on one’s own research and allow for fruitful coproduction of knowledge in the project team on how to improve the societal relevance of projects.

What techniques have you used to plan, and reflect on, making your research socially relevant?

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Christian Pohl (biography)

pius-krutli
Pius Krütli (biography)

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Michael Stauffacher (biography)

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Let’s stop measuring and start improving

Community member post by Louise Locock

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Louise Locock (biography)

When we’re trying to improve the experience of health care, social care and other services users, is there a fast, rigorous way to include their perspectives that doesn’t involve repeatedly collecting new data from them and their families?

Measuring, understanding and improving people’s experience of services has become a priority. There is now an international focus (at least in the West) on person-centred care. The English National Health Service has led the way among health systems by introducing the first nationally mandated patient survey.

Despite the strong political and organisational focus on improving care, reports of unsatisfactory experience continue in even the best funded care systems. Continue reading

A new boundary object to promote researcher engagement with policy makers / Un nuevo objeto frontera para promover la colaboración de los investigadores con los tomadores de decisiones

Community member post by María D. López Rodríguez

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María D. López Rodríguez (biography)

A Spanish version of this post is available

Can boundary objects be designed to help researchers and decision makers to interact more effectively? How can the socio-political setting – which will affect decisions made – be reflected in the boundary objects?

Here I describe a new context-specific boundary object to promote decision making based on scientific evidence. But first I provide a brief introduction to boundary objects.

What is a ‘boundary object’?

In transdisciplinary research, employing a ‘boundary object’ is a widely used method to facilitate communication and understanding among stakeholder groups with different epistemologies. Boundary objects are abstract tools adaptable to different perspectives and across knowledge domains to serve as a means of symbolic communication. Continue reading

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

Community member post by Jan Kwakkel

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Jan Kwakkel (biography)

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are. Continue reading

Toolkits for transdisciplinary research

Community member post by Gabriele Bammer

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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

Good practices in system dynamics modelling

Community member post by Sondoss Elsawah and Serena Hamilton

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Sondoss Elsawah (biography)

Too often, lessons about modelling practices are left out of papers, including the ad-hoc decisions, serendipities, and failures incurred through the modelling process. The lack of attention to these details can lead to misperceptions about how the modelling process unfolds.

serena-hamilton
Serena Hamilton (biography)

We are part of a small team that examined five case studies where system dynamics was used to model socio-ecological systems. We had direct and intimate knowledge of the modelling process and outcomes in each case. Based on the lessons from the case studies as well as the collective experience of the team, we compiled the following set of good practices for systems dynamics modelling of complex systems. Continue reading