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This is the published version of a paper published in Acta Sociologica.
Citation for the original published paper (version of record):
Lidskog, R., Standring, A. (2020)
The institutional machinery of expertise: Producing facts, figures and futures in
COVID-19
Acta Sociologica, 63(4): 443-446
https://doi.org/10.1177/0001699320961807
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The institutional machinery
of expertise: Producing facts,
figures and futures in COVID-19
Rolf Lidskog
Environmental Sociology Section, O¨ rebro University, Sweden
Adam Standring
Environmental Sociology Section, O¨ rebro University, Sweden
Abstract
This paper examines the sociological importance of expert knowledge in the COVID-19
pandemic. Through this expertise, it is possible to follow patterns of infections, fatalities and
recoveries almost in real time, and this knowledge is crucial for countries when deciding on
relevant governmental strategies to control the spread. The paper stresses that there was a
strong institutional machinery of expertise for data production and dissemination, and despite
rather different national ambitions in detection strategies (both concerning infections and
mortalities), this machinery produced facts and figures as though they were measured
uniformly.
Keywords
COVID-19, expertise, forecasting, immutable mobiles, mathematical modelling, models,
uncertainty
One of the most visible and immediate effects of COVID-19 has been the way in which it has compressed the time in which governments have had to make decisions. The necessary and urgent action needed to stop the spread of the virus and ‘flatten the curve’ has meant that there was no time to wait for further knowledge, to investigate uncertainties or to broaden debates around the relative merits of different options. Lockdowns were enacted and borders closed without strong evidence of their efficacy or efficiency and without knowing the broader (social and economic) consequences. Obviously, there are great national variations: some countries developed strong responses instantly, based on legislative measures (e.g. China, Italy, Spain, South Africa), whereas others developed less strong ones
Corresponding Author:
Rolf Lidskog, Environmental Sociology Section, School of Humanities, Education and Social Sciences, O¨ rebro University, 702 81, O¨ rebro, Sweden.
Email: rolf.lidskog@oru.se
Acta Sociologica 2020, Vol. 63(4) 443–446
ªThe Author(s) 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0001699320961807 journals.sagepub.com/home/asj
(e.g. Brazil, US); some countries were late to respond (e.g. UK) whereas others were fast but put greater trust in soft measures (e.g. Sweden). Despite national variations, the advice provided by the international expert authority World Health Organization (WHO) – and supported by most national expert bodies and researchers – forcefully argued for the need to act swiftly and thereby restore control over the viral outbreak.
The way crises are regulated – and even recognised as such – is interrelated with the particular governance configuration (Lidskog et al., 2020a). Of fundamental importance in this relationship is the way that knowledge is produced and used by and for governments. Against a popular understand-ing of science as separate from policy, producunderstand-ing knowledge about society from an external and objective position, Sociology of Science Knowledge (SSK) stresses that it is created through agency, instruments and interests (Bloor, 1976; Latour and Woolgar, 1979). In short, science is a social practice, making it possible (and important) for sociology to analyse it as such, not only as a means to better understand our societies but as a means to provide better science and responses to social problems (Hulme et al., 2020).
The pandemic brought into focus the strong institutional machinery of expertise already in place and ready to deliver facts and figures on the current situation. It showed that this machinery extends beyond spatial boundaries, with national agencies and committees connected both up to international bodies such as WHO and down to local and regional bodies. It also showed that these facts and figures – on infections, recoveries and fatalities of COVID-19 – are simultaneously products of and productive of government responses: infections are reliant on testing strategies and mortality figures differ depending on criteria reflective of different national ambitions in detection strategies (Sismondo, 2020). In each case, this influences how governments understand and justify their responses.
Figures produced are not merely descriptive; many are intended to be predictive, constructing futures in the face of an uncertain present: mathematical modelling has been extensively used to show trends and forecast the future (Gallaghan, 2020; Rhodes et al., 2020). These projections of the future spread of COVID-19 dominate the discussion, accompanied with messages about the direction, speed and severity of the problem but without details of how units are defined and measures are performed. Figures and graphs are provided as if they measure uniformly but, in contrast to many environmental phenomena where monitoring stations automatically measure environmental data (such as air quality, ozone deple-tion or greenhouse gases in the atmosphere), epidemiological figures are primarily based on manually collected and coded data (Jasanoff, 2020). Whereas issues of uncertainty have been a chronic headache for climate issues, to the extent that the Intergovernmental Panel on Climate Change (IPCC) has developed an explicit and structured way to manage them (see IPCC, 2010), uncertainty has played a minor role in responding to COVID-19. On the contrary, uncertainty and unknowns have actively been used to legitimise (differentiated) governmental action. Hasty lockdowns of cities, regions and whole countries have been based on forecasting models, often not subjected to peer review (Ensenrink and Kupferschmidt, 2020) or with any relation to the social (and political) nature of the models. As a recent ‘manifesto for models’ makes explicit, ‘good modelling cannot be done by modellers alone. It is a social activity’ (Nature, 2020).
Graphs, projections, models and maps work seductively to present dynamic, ambiguous and con-tingent issues as ‘hard facts’: empirical measures that are resistant to public and political debate (Lidskog et al., 2020b). They become ‘immutable mobile’ (Latour, 1987), permanent and stable phenomena that easily travel between contexts and place – from the world politics of WHO and the UN to national discussions on closing borders to casual chats in the workplace, school and home. Around the world, people and organisations can follow infection patterns in almost real time and down to the level of individual cities. But behinds these numbers are an enormous machine of people, resources, technologies and institutions that have to collect data, decide on how to handle the poor quality of this data, manage (including ignore) issues of uncertainty and decide on how to visualise these figures and projections in order to make them understandable and useful.
These institutional machines of expertise are, thus, social machines. When we look at different national responses to COVID-19 – including assessing successes and failures – it is therefore crucial not to take national facts and figures as given but rather as constructions of these social machines.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Rolf Lidskog https://orcid.org/0000-0001-6735-0011
References
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Author biographies
Rolf Lidskog is professor in sociology at O¨ rebro University, Sweden. His research interest is environ-mental policy and politics on international and national level, especially the role of expertise in envi-ronmental politics. He has published extensively in journals on topics such as envienvi-ronmental sociology, environmental politics and policy, risk regulation and science-policy relations. He is co-author of the book Transboundary Risk Governance (Earthscan, 2009) and co-editor of Governing the Air: The Dynamics of Science, Policy, and Citizen Interaction (the MIT Press, 2011).
Adam Standring is a postdoctoral researcher in environmental sociology at O¨ rebro University, Sweden. He researches the political sociology of expertise in international organisations and he is currently researching (with Rolf Lidskog) the understandings and practices of expertise in the IPCC. He has published on austerity, drug policy, neoliberalism and governance in journals including Environment & Planning C: Politics & Space, Policy & Politics and WIREs Climate Change & Dialogues in Human Geography. His recent co-edited collection (with Jim Buller, Pinar Donmez and Matt Wood) is Com-paring Strategies of Depoliticization in Europe: Governance, Resistance & Anti-politics (Palgrave Macmillan, 2018).
