Integration and Implementation Insights

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.

Problem framing and co-creation

August 24, 2022October 27, 2016

By Graeme Nicholas

How can people with quite different ways of ‘seeing’ and thinking about a problem discover and negotiate these differences?

A key element of co-creation is joint problem definition. However, problem definition is likely to be a matter of perspective, or a matter of how each person involved ‘frames’ the problem. Differing frames are inevitable when participants bring their differing expertise and experience to a problem. Methods and processes to support co-creation, then, need to manage the coming together of people with differing ways of framing the problem, so participants can contribute to joint problem definition.

Key readings about interdisciplinarity / Lecturas clave sobre interdisciplina

July 18, 2023October 25, 2016

By Bianca Vienni

An English version of this post is available

Si ud tuviera que elegir un conjunto de textos clave sobre la interdisciplina para traducir a otro idioma y utilizarlos en un grupo de discusión, ¿cuáles serían? Esa fue la tarea que nos propusimos en el Espacio Interdisciplinario de la Universidad de la República (UdelaR) en Uruguay.

Elegimos once textos que capturan la diversidad de enfoques sobre la interdisciplina y que también constituyen un punto de referencia para la producción académica.

Improving health care services through Experience-based Co-design

September 11, 2024October 20, 2016

By Glenn Robert and Annette Boaz

authors_glenn-robert_annette-boaz — 1. Glenn Robert (biography)
2. Annette Boaz (biography)

There is lots of talk about the potential of co-creation as an approach to improving public services, but what does it actually look like (and do) in practice?

We describe one specific approach that has been used extensively for improving the quality of health care services: Experience-based Co-design.

Key Features and Stages

Experience-based Co-design draws on elements of participatory action research, user-centred design, learning theory and narrative-based approaches to change.

The key features of Experience-based Co-design are that it:

places patients at the heart of a quality improvement effort working alongside staff to improve services
maintains a focus on designing experiences (not just systems or processes).

It has six stages.

Stage 1 involves establishing the governance and project management arrangements.

Six lessons about change that affect research impact

September 5, 2024October 18, 2016

By Gabriele Bammer

What do researchers need to know about change to help our research have greater impact? What kind of impact is it realistic to expect? Will understanding change improve the ways we assess research impact?

The six lessons described here illustrate some of the complexities inherent in understanding and trying to influence change.

#1. Research findings enter a dynamic environment, where everything is changing all the time

As researchers we often operate as if the world is static, just waiting for our findings in order to decide where to head next. Instead, for research to have impact, researchers must negotiate a constantly changing environment.

A co-creation challenge: Aligning research and policy processes

August 24, 2022October 13, 2016

By Katrin Prager

How does the mismatch between policy and research processes and timelines stymie co-creation? I describe an example from a project in Sachsen-Anhalt state in Germany, along with lessons learnt.

The project, initiated by researchers, aimed to use a more participatory approach to developing agri-environmental schemes, in order to improve their effectiveness. Officers from the Agricultural Payments department of the Sachsen-Anhalt Ministry for Agriculture were invited to participate in an action research project that was originally conceived to also involve officers from the Conservation department of the same ministry, farmer representatives and conservation groups.

The ‘methods section’ in research publications on complex problems – Purpose

September 5, 2024October 11, 2016

By Gabriele Bammer

Do we need a protocol for documenting how research tackling complex social and environmental problems was undertaken?

Usually when I read descriptions of research addressing a problem such as poverty reduction or obesity prevention or mitigation of the environmental impact of a particular development, I find myself frustrated by the lack of information about what was actually done. Some processes may be dealt with in detail, but others are glossed over or ignored completely.

For example, often such research brings together insights from a range of disciplines, but details may be scant on why and how those disciplines were selected, whether and how they interacted and how their contributions to understanding the problem were combined. I am often left wondering about whose job it was to do the synthesis and how they did it: did they use specific methods and were these up to the task? And I am curious about how the researchers assessed their efforts at the end of the project: did they miss a key discipline? would a different perspective from one of the disciplines included have been more useful? did they know what to do with all the information generated?

Should I trust that model?

September 15, 2023October 6, 2016

By Val Snow

How do those building and using models decide whether a model should be trusted? While my thinking has evolved through modelling to predict the impacts of land use on losses of nutrients to the environment – such models are central to land use policy development – this under-discussed question applies to any model.

In principle, model development is a straightforward series of steps:

• Specification: what will be included in the model is determined conceptually and/or quantitatively by peers, experts and/or stakeholders and the underlying equations are decided

• Coding: the concepts and equations are translated into computer code and the code is tested using appropriate software development processes

From integration to interaction: A knowledge ecology framework

September 15, 2023October 4, 2016

By Zoë Sofoulis

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.

Creating a pragmatic complexity culture / La creación de una cultura pragmática de la complejidad

August 24, 2022September 29, 2016

By Cristina Zurbriggen

An English version of this post is available

¿Cómo pueden los gobiernos, las comunidades y el sector privado efectivamente trabajar juntos para lograr un cambio social hacia el desarrollo sostenible?

En este blog describo los procesos claves que permitieron a Uruguay lograr uno de los regímenes más avanzados de protección del suelo de tierras de cultivo de secano en el mundo. Una explicación del proceso es la creación de una cultura pragmática de la complejidad, una cultura inclusiva, deliberativa que reconoce la naturaleza compleja del problema y abraza el potencial de lo posible.

Five principles for achieving impact

September 5, 2024September 27, 2016

By Mark Reed

What key actions can help research have impact? Interviews with 32 researchers and stakeholders across 13 environmental management research projects lead to the five principles and key issues described below (Reed et al., 2014).

1. Design:

• Understand what everyone wants. This can help in managing expectations of different stakeholders and project members and identifying potential issues/problems early on.
• Understand the context of the project. Use local characteristics, traditions, norms and past experiences as a starting point for planning the project.
• Take your time. Knowledge exchange is time consuming if done properly.
• Design your knowledge exchange activities carefully. Spend time researching the context, the stakeholders, and possible approaches. Design for flexibility, get feedback, and adapt your plans to suit changing circumstances.

ICTAM: Bringing mental models to numerical models

August 24, 2022September 22, 2016

By Sondoss Elsawah

How can we capture the highly qualitative, subjective and rich nature of people’s thinking – their mental models – and translate it into formal quantitative data to be used in numerical models?

This cannot be addressed by a single method or software tool. We need multi-method approaches that have the capacity to take us through the learning journey of eliciting and representing people’s mental models, analysing them, and generating algorithms that can be incorporated into numerical models.

More importantly, this methodology should allow us to see in a transparent way the progression on this learning journey.