Evaluation

Five structural levers to reopen feedback loops that are resistant to external evidence

By Lachlan S. McGill.

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Lachlan S. McGill (biography)

When feedback loops have become resistant to external evidence, what are some potential ways of intervening to reopen them?

This i2Insights contribution builds on my previous post which covers understanding why feedback loops can become resistant to external evidence and how to diagnose such a structural problem.

Here I introduce five structural ways to intervene in such a closed feedback loop. These are structural levers, each targeting a different aspect of how signals flow, how authority is allocated, and how evaluative standards are defined.

One practical note before beginning. Applying the interventions below often requires institutional authority, coalition building, or regulatory support, so that isolated actors may not be able to deploy them fully, leaving the problematic dominant structure intact.

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Understanding and diagnosing when feedback loops become resistant to external evidence

By Lachlan S. McGill.

lachlan-mcgill
Lachlan S. McGill (biography)

Why does better evidence sometimes fail to improve decision making? How can we tell if this is caused by feedback loops becoming resistant to external evidence?

Understanding how structural patterns become problematic

In most organisations, decisions are embedded in feedback loops that connect indicators, incentives, and authority structures. These loops determine what counts as success, which signals influence decisions, and how performance is evaluated over time.

When feedback loops are well aligned with system goals, they support learning. However, feedback loops can also evolve in ways that reinforce a narrow definition of success. This is generally associated with a system relying on a small number of indicators to guide decisions.

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A framework for considering context in evaluation of transdisciplinary research projects

By Julia Schegg, Rea Pärli, Manuel Fischer and Eva Lieberherr.

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1. Julia Schegg (biography)
2. Rea Pärli (biography)
3. Manuel Fischer (biography)
4. Eva Lieberherr (biography)

How do contextual factors influence the effects of transdisciplinary research projects? How can assessment of transdisciplinary research move from only considering outcome effectiveness to understanding the reasons behind how and why something works?

Our framework considers both context and the effects of transdisciplinary research projects, as summarised in the figure below. Each aspect of the framework is described in more detail.

Context

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Six key steps for stakeholder engagement

By Khara Grieger, Kimberly Bourne, Alison Deviney and Nourou Barry.

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1. Khara Grieger (biography)
2. Kimberly Bourne (biography)
3. Alison Deviney (biography)
4. Nourou Barry (biography)

How can you systematically plan stakeholder engagement? What are the key issues that need to be considered? What guiding questions can help? 

STEP 1: Identify and clarify engagement goals

Spend time at the beginning of the engagement process to clearly identify why you are engaging with stakeholders. Common goals include sharing knowledge or information; collecting insights, perspectives, or information from stakeholders; and co-creating or co-designing solutions. Other potential goals may include building trust and improving transparency, enhancing collaborations and partnerships, and improving the implementation of decisions.

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A self-assessment checklist to improve interdisciplinarity in research projects / Un questionnaire d’auto-évaluation pour améliorer les projets de recherche interdisciplinaire

By Flore Nonchez.

A French version of this post is available

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Flore Nonchez (biography)

How can researchers improve the quality of their interdisciplinary proposals? What would help in clarifying project formulation, enriching description, maximizing relevance, facilitating workplan implementation (by anticipating possible difficulties) and enhancing impact?

The self-assessment checklist provided below aims to help project leaders and their research teams systematically consider the specific interdisciplinary aspects of an interdisciplinary research project, whatever their original discipline or experience of interdisciplinarity.

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Transdisciplinary research with and for artificial intelligence

By Florian Keil, Melina Stein and Flurina Schneider.

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1. Florian Keil’s biography
2. Melina Stein (biography)
3. Flurina Schneider (biography)

Is artificial intelligence, a technology aggressively advertised as the ultimate cure-all, fundamentally incompatible with transdisciplinarity and its decades-old insight that the “wicked” problems of the real world do not lend themselves to one-dimensional solutions? Should transdisciplinary research outright reject a technology that is already undermining efforts to achieve social and environmental justice? Or can artificial intelligence actually support transdisciplinary research when used responsibly?

Using artificial intelligence in transdisciplinary research requires a critical mindset

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Evaluation criteria for transdisciplinary research

This i2Insights contribution has been retracted.

For an excellent framework on evaluation criteria for transdisciplinary research, see:

Belcher, B. M., Rasmussen, K. E., Kemshaw, M. R. and Zornes, D. A. (2016). Defining and Assessing Research Quality in a Transdisciplinary Context. Research Evaluation, 25, 1–17. (Online – open access) (DOI): https://doi.org/10.1093/reseval/rvv025

with an updated and refined version available at: Transdisciplinary-Research-Quality-Assessment-Framework-2.0.pdf. 

 

What transdisciplinary researchers should know about evaluation: Origins and current state

By Wolfgang Beywl and Amy Gullickson

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1. Wolfgang Beywl (biography)
2. Amy Gullickson (biography)

Efforts to develop evaluation in transdisciplinary research have mostly been conducted without reference to the evaluation literature, effectively re-inventing and re-discussing key concepts. What do transdisciplinary researchers need to know to build on the in-depth knowledge available in evaluation science?

Here we add to other key contributions about evaluation in i2Insights, especially:

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How to systematically design transdisciplinary project evaluation

By Emilia Nagy and Martina Schäfer

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1. Emilia Nagy (biography)
2. Martina Schäfer (biography; photo source: Landtag Brandenburg)

How can the formative, ie. process, evaluation of transdisciplinary research projects best incorporate the likely link between process and outcomes in such research? What are some useful approaches for developing an effective evaluation plan with a lens of impact orientation?

We describe how to systematically formulate criteria and indicators for the evaluation of transdisciplinary projects by combining:

  • impactful research practices (Lux et al., 2019)
  • impact heuristics (Schäfer et al., 2021)
  • theory-of-change method (Belcher et al., 2019).

The combination of these approaches provides a strong foundation for impact orientation in all project phases.

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Eleven success factors for transdisciplinary real-world labs

By Niko Schäpke, Oskar Marg, Matthias Bergmann, Franziska Stelzer and Daniel J. Lang

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1. Niko Schäpke; 2. Oskar Marg; 3. Matthias Bergmann; 4. Franziska Stelzer; 5. Daniel Lang (biographies)

What is required for transdisciplinary real-world laboratories (labs) to successfully tackle and achieve long-term societal change? How can they make the change process transferable? What is required of the societal and scientific actors?

We discuss eleven success factors to facilitate successful transdisciplinary collaboration and to achieve desired societal effects. These are based on an accompanying research project, which supported and observed several real-world labs, aiming to develop overarching insights on methods and success factors. The accompanying research project also provided consultancy to strengthen the implementation activities of the real-world labs and supported the networking of the labs, including with labs in other countries.

The factors presented below should be seen as generic recommendations to increase the likelihood of success. As each lab has a unique character, factors should be considered, deliberated and addressed for each lab on an individual basis.

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Overcoming the mismatch between goals and outcomes in knowledge exchange

By Denis Karcher and Chris Cvitanovic

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1. Denis Karcher (biography)
2. Chris Cvitanovic (biography)

How well do researchers achieve the research impacts they aim for? And if there is a mismatch, does it matter?

Together with colleagues (Karcher et al., 2021), we systematically searched for and reviewed nearly 400 studies that described goals and outcomes that were claimed for knowledge exchange at the science-policy interface. Although our focus was on the environmental sciences, the results may be more widely useful.

Big ambitions

The eight top goals that studies described for their knowledge exchange activities were:

1. Usability, eg., that the interaction with policy makers and/or the knowledge created were credible, legitimate, relevant, and timely (458 references).

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Stakeholder engagement primer: 9. Evaluating engagement

By Gabriele Bammer

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How can the effectiveness of stakeholder engagement be judged? How can the outcomes be assessed? How much effort should go into such evaluation?

How and when to evaluate

In any stakeholder engagement there is no shortage of aspects that could be evaluated. The challenge is aligning the audience for the evaluation, the key issues to be assessed and the available resources.

For example, if a research team is interested in learning from what went wrong in a stakeholder engagement (for instance, if a stakeholder stopped participating or became hostile) and has no money set aside for evaluation, it might rely on self-reflection and anecdotal evidence to figure out what happened.

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