Kat Smith (Strathclyde), Sudeepa Abeysinghe (Social Policy, Edinburgh) and Christina Boswell (PIR, Edinburgh) presented three complementary perspectives on the impact of Covid-19 on the study of the relationship between science, knowledge and policy.
Christina Boswell noted the extent and unprecedented level of granular coverage of science and the scientific debate around Covid-19 in the media and public debate. At the same time, there is a dependence on expert knowledge around the virus, in particular at the level of the UK government, that points to symbolic uses: government representatives are flanked by experts at daily press conferences, and the mantra is that any decision is “led by the science”. This goes beyond symbolic uses of science to bolster policy choices; with Covid-19, science has become an insurance policy for the government.
Two risks emerge from these observations. The first is that science will disappoint because of unrealistic expectations. In the medium to long term, this could lead to an erosion of trust in science. This extends to individual scientists too. The second risk emerges from a paradox: science and scientists need to be independent to work as a resource and to ensure the credibility of science; science needs to retain its fallibility and can’t be responsible for prescribing courses of action. But in the context of Covid-19, science has appeared closer to political decision-making, and in support of policy and decision-making, it undermines the resource. There is a high degree of dependence on science to resolve the Covid related issues; however, the action of deploying this resource undermines the resource. This is a paradox that can be observed in other policy and decision-making areas, such as migration for instance. This led Christina Boswell to raise a central question: how can we build trust in these models to make sure they are relevant as a resource and without undermining their credibility and legitimacy? For Christina Boswell, there is a need to explore further governance models of the interface between science and policy and decision-making.
Kat Smith’s thinking and discussions with Justin Parkhurst and colleagues around Covid-19 have centred a lot around the role of legitimacy and the pressure on the evidence-advisory systems in the current times. The legitimacy of the evidence-advisory systems takes on three aspects: technical legitimacy, political legitimacy and process legitimacy. In terms of technical legitimacy, in pandemics, decision-makers appear to be naturally drawn towards epidemiologists and models that are future-orientated, presenting quantified data, no doubt because it provides “something that they can hold onto”. But these models are very difficult to scrutinise. Kat Smith is particularly concerned about the way in which the absence of knowledge is recognised and made clear in these models and the way the results are being communicated more broadly. This leads to the second aspect of legitimacy: in terms of political legitimacy, more delineating should be done between evidence-led decisions and politically motivated ones. Decision-makers focus strongly on modelling and it isn’t always clear that models are used as guidance only. This means that the assumptions about the environment intrinsic to these models are not made explicit by decision-makers. This has serious implications, notably for broader socio-economic issues. And finally, in terms of process legitimacy, transparency is key to ensure that there is both scientific and public scrutiny around decision-making about pandemic responses (which tend to sit outside normal legitimacy processes, such as elections and party manifestos). Accountability systems in these pressured times of rapid and major policy developments cannot function without transparency. Both scientific and public scrutiny could be usefully strengthened in the UK and it was notable that the limitations of current arrangements were cited by Sir David King in explaining his decision to convene the Independent SAGE group.
Kat Smith provided a final reflection stemming from her conversations with colleagues working on COVID-19 responses in policy settings, which underlined once again her major concern around how evidence, and particularly modelling, is being portrayed in the public debate. Echoing Christina Boswell’s points, she noted many of the policy colleagues she had spoken to were concerned about the long-term implications for public trust in science.
Sudeepa Abeysinghe first reflected on how Covid-19 subverts expectations around how scientific uncertainty plays out in public health interventions. The virus and its impacts were, and to some extent continue to be, underpinned by scientific uncertainty. Epidemiological modelling was – at least initially – based upon analogous, anecdotal, theoretical and speculative evidence. Under such circumstances, we tend to see the blurring of boundaries between politics and knowledge under post-normal forms of science. This, for instance, played out in the case of the WHO and H1N1: Epidemiological uncertainty was reframed as a politically motivated decision. However, instead of scientific uncertainty providing a means of contestation, we instead experienced a consolidation of the ‘factiness’ of the case. For many, the science-based nature of interventions, as asserted in political messaging, was taken-for-granted. This is despite the messiness of the data and modelling as recounted by the scientists themselves. This prompts the question: why is this the case?
And secondly, Sudeepa Abeysinghe also reflected on the simplified packaging of scientific evidence in government guidance and publications. Drawing on some initial empirical work in relation to Covid-19 in Indonesia, Sudeepa Abeysinghe suggests that instead of a knowledge deficit, the public may be engaging in complex decision-making weighing different aspects against each other, notably bringing in socio-economic concerns too. Sudeepa Abeysinghe concludes by raising the question: why and how are issues of public health intervention still framed and discussed as a deficit of knowledge of the public?
A number of points also arose from responses to questions during the seminar. A first question prompted reflections on science coming from China. Christina Boswell noted that there is a discourse that data coming from China is not trustworthy and suggested that there is a tendency to the nationalisation of scientific advice in the public debate. National competitiveness of science is remerging. In the UK, it also raises questions about funding research.
There was also a question on why there is such reluctance to admit to uncertainty. Kat Smith suggested that this is part an evidence-advisory systems issue, part an institutional issue. Do these systems look at broad types of knowledge beyond epidemiology? For instance, logistics were not taking into account in the delivery of PPE initially. Secondly, there is a fragmentation of governance; in Scotland for instance, there are many different groups of scientific advisers that have been set up and the entire civil services has been rearranged as a resit of Covid-19. This creates a very fragmented decision-making landscape.
There was also a reflection on the way in which the role of experts has changed as a result of Covid-19. A much wider range of experts is now involved, with some having more influence and traction because of social media and salience. There may be an indirect effect on the institutionalisation of the use of science.
In relation to legitimacy, concerns were raised in view of the shift of the responsibility for risk onto the public and how this may feed into existing inequalities for instance. More broadly, it is important to note that we are only partially into this crisis.
About the authors
Sudeepa Abeysinghe is Lecturer in Global Health Policy at the University of Edinburgh.
This blog post is a summary of the SKAPE Seminar on 24 June 2020.