November 2016 – Integration and Implementation Insights

Storytelling ethnography as a way of doing transdisciplinary research

September 11, 2024November 29, 2016

By Jane Palmer

Storytelling ethnography is a valuable tool if your research traverses several disciplines and aims for insights that transcend all of them. Stories not only integrate knowledge from diverse disciplines, but can also “change the way people act, the way they use available knowledge” (Griffiths 2007).

The special qualities of transdisciplinarity are:

its potential for integrative inquiry and emergent solutions,
its engagement with community and other non-academic knowledges, and
the breadth of its outcomes for researchers, participants and the wider community.

These are also qualities of what I call storytelling ethnography.

Advocate or Honest Broker?

November 30, 2022November 22, 2016

By Gabriele Bammer

To mark the first anniversary of the Integration and Implementation Insights blog, we launch an occasional series of “synthesis blog posts” drawing insights across blog posts on related topics.

What is our social obligation as researchers to see our findings implemented? And how should we do it? When is it appropriate to advocate loudly to drive change? When should we focus on informing decision makers, stepping back ourselves from direct action? How can we know that our research is ‘good enough’ to act on and not compromised by our own values, interests, cognitive biases and blind spots?

Learning through modeling

October 9, 2023November 17, 2016

By Kirsten Kainz

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.

The integrative role of landscape

July 4, 2024November 15, 2016

By David Brunckhorst, Jamie Trammell and Ian Reeve

authors_mosaic_david-brunckhorst_jamie-trammell_ian-reeve — 1. David Brunckhorst (biography)
2. Jamie Trammell (biography)
3. Ian Reeve (biography)

Landscapes are the stage for the theatre of human-nature interactions. What does ‘landscape’ mean and what integrative function does it perform?

What is landscape?

Consider a painting of a landscape or look out a window. We imagine, interpret and construct an image of the ‘landscape’ that we see. It’s not surprising that landscapes (like the paintings of them) are valued through human perceptions, and evolve through closely interdependent human-nature relationships. Landscapes are co-constructed by society and the biophysical environment. Landscape change is, therefore, a continuous reflection of the evolving coupled responses of environment and institutions. Landscapes are especially meaningful to those who live in them.

Uncertainty in participatory modeling – What can we learn from management research?

August 24, 2022November 10, 2016

By Antonie Jetter

I frequently struggle to explain how participatory modeling deals with uncertainty. I found useful guidance in the management literature.

After all, participatory modeling projects and strategic business planning have one commonality – a group of stakeholders and decision-makers aims to understand and ultimately influence a complex system. They do so in the face of great uncertainty that frequently cannot be resolved – at least not within the required time frame. Businesses, for example, have precise data on customer behavior when their accountants report on annual sales. However, by this time, the very precise data is irrelevant because the opportunity to influence the system has passed.

Collaboration, difference and busyness

November 30, 2022November 8, 2016

By Gabriele Bammer

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.

Model complexity – What is the right amount?

August 24, 2022November 3, 2016

By Pete Loucks

How does a modeler know the ’optimal’ level of complexity needed in a model when those desiring to gain insights from the use of such a model aren’t sure what information they will eventually need? In other words, what level of model complexity is needed to do a job when the information needs of that job are uncertain and changing?

Simplification is why we model. We wish to abstract the essence of a system we are studying, and estimate its likely performance, without having to deal with all its detail. We know that our simplified models will be wrong. But, we develop them because they can be useful. The simpler and hence the more understandable models are the more likely they will be useful, and used, ‘as long as they do the job.’

Four things everyone should know about ignorance

April 13, 2021November 1, 2016

By Michael Smithson

“Ignorance” is a topic that sprawls across a grand variety of disciplines, professions and problem domains. Many of these domains have their own perspective on the unknown, but these are generally fragmentary and often unconnected from one another. The topic lacks a home. Until fairly recently, it was a neglected topic in the humanities and human sciences.

I first started writing about it in the 1980’s (e.g., my book-length treatment, Ignorance and Uncertainty: Emerging Paradigms), but it wasn’t until 2015 that the properly compiled interdisciplinary Routledge International Handbook on Ignorance Studies (Gross and McGoey 2015) finally appeared.