Here is the powerpoint that I tend to use to inform discussions with civil servants (CS). I first used it for discussion with CS in the Scottish and UK governments, followed by remarkably similar discussions in parts of New Zealand and Australian government. Partly, it provides a way into common explanations for gaps between the supply of, and demand for, research evidence. However, it also provides a wider context within which to compare abstract and concrete reasons for those gaps, which inform a discussion of possible responses at individual, organisational, and systemic levels. Some of the gap is caused by a lack of effective communication, but we should also discuss the wider context in which such communication takes place.
I begin by telling civil servants about the message I give to academics about why policymakers might ignore their evidence:
- There are many claims to policy relevant knowledge.
- Policymakers have to ignore most evidence.
- There is no simple policy cycle in which we all know at what stage to provide what evidence.
In such talks, I go into different images of policymaking, comparing the simple policy cycle with images of ‘messy’ policymaking, then introducing my own image which describes the need to understand the psychology of choice within a complex policymaking environment.
Under those circumstances, key responses include:
- framing evidence in terms of the ways in which your audience understands policy problems
- engaging in networks to identify and exploit the right time to act, and
- venue shopping to find sympathetic audiences in different parts of political systems.
However, note the context of those discussions. I tend to be speaking with scientific researcher audiences to challenge some preconceptions about: what counts as good evidence, how much evidence we can reasonably expect policymakers to process, and how easy it is to work out where and when to present evidence. It’s generally a provocative talk, to identify the massive scale of the evidence-to-policy task, not a simple ‘how to do it’ guide.
In that context, I suggest to civil servants that many academics might be interested in more CS engagement, but might be put off by the overwhelming scale of their task, and – even if they remained undeterred – would face some practical obstacles:
- They may not know where to start: who should they contact to start making connections with policymakers?
- The incentives and rewards for engagement may not be clear. The UK’s ‘impact’ agenda has changed things, but not to the extent that any engagement is good engagement. Researchers need to tell a convincing story that they made an impact on policy/ policymakers with their published research, so there is a notional tipping point of engagement in which it reaches a scale that makes it worth doing.
- The costs are clearer. For example, any time spent doing engagement is time away from writing grant proposals and journal articles (in other words, the things that still make careers).
- The rewards and costs are not spread evenly. Put most simply, white male professors may have the most opportunities and face the fewest penalties for engagement in policymaking and social media. Or, the opportunities and rewards may vary markedly by discipline. In some, engagement is routine. In others, it is time away from core work.
In that context, I suggest that CS should:
- provide clarity on what they expect from academics, and when they need information
- describe what they can offer in return (which might be as simple as a written and signed acknowledgement of impact, or formal inclusion on an advisory committee).
- show some flexibility: you may have a tight deadline, but can you reasonably expect an academic to drop what they are doing at short notice?
- Engage routinely with academics, to help form networks and identify the right people you need at the right time
These introductory discussions provide a way into common descriptions of the gap between academic and policymaker:
- Technical languages/ jargon to describe their work
- Timescales to supply and demand information
- Professional incentives (such as to value scientific novelty in academia but evidential synthesis in government
- Comfort with uncertainty (often, scientists project relatively high uncertainty and don’t want to get ahead of the evidence; often policymakers need to project certainty and decisiveness)
- Assessments of the relative value of scientific evidence compared to other forms of policy-relevant information
- Assessments of the role of values and beliefs (some scientists want to draw the line between providing evidence and advice; some policymakers want them to go much further)
To discuss possible responses, I use the European Commission Joint Research Centre’s ‘knowledge management for policy’ project in which they identify the 8 core skills of organisations bringing together the suppliers and demanders of policy-relevant knowledge
However, I also use the following table to highlight some caution about the things we can achieve with general skills development and organisational reforms. Sometimes, the incentives to engage will remain low. Further, engagement is no guarantee of agreement.
In a nutshell, the table provides three very different models of ‘evidence-informed policymaking’ when we combine political choices about what counts as good evidence, and what counts as good policymaking (discussed at length in teaching evidence-based policy to fly). Discussion and clearer communication may help clarify our views on what makes a good model, but I doubt it will produce any agreement on what to do.
In the latter part of the talk, I go beyond that powerpoint into two broad examples of practical responses:
The Narrative Policy Framework describes the ‘science of stories’: we can identify stories with a 4-part structure (setting, characters, plot, moral) and measure their relative impact. Jones/ Crow and Crow/Jones provide an accessible way into these studies. Also look at Davidson’s article on the ‘grey literature’ as a rich source of stories on stories.
On one hand, I think that storytelling is a great possibility for researchers: it helps them produce a core – and perhaps emotionally engaging – message that they can share with a wider audience. Indeed, I’d see it as an extension of the process that academics are used to: identifying an audience and framing an argument according to the ways in which that audience understands the world.
On the other hand, it is important to not get carried away by the possibilities:
- My reading of the NPF empirical work is that the most impactful stories are reinforcing the beliefs of the audience – to mobilise them to act – not changing their minds.
- Also look at the work of the Frameworks Institute which experiments with individual versus thematic stories because people react to them in very different ways. Some might empathise with an individual story; some might judge harshly. For example, they discusse stories about low income families and healthy eating, in which they use the theme of a maze to help people understand the lack of good choices available to people in areas with limited access to healthy food.
- Evidence for advocacy
The article I co-authored with Oxfam staff helps identify the lengths to which we might think we have to go to maximise the impact of research evidence. Their strategies include:
- Identifying the policy change they would like to see.
- Identifying the powerful actors they need to influence.
- A mixture of tactics: insider, outsider, and supporting others by, for example, boosting local civil society organisations.
- A mix of ‘evidence types’ for each audience
- Wider public campaigns to address the political environment in which policymakers consider choices
- Engaging stakeholders in the research process (often called the ‘co-production of knowledge’)
- Framing: personal stories, ‘killer facts’, visuals, credible messenger
- Exploiting ‘windows of opportunity’
- Monitoring, learning, trial and error
In other words, a source of success stories may provide a model for engagement or the sense that we need to work with others to engage effectively. Clear communication is one thing. Clear impact at a significant scale is another.