aPatrick Vallance, a former UK Chief Scientific Advisor (GCSA), who sat on the UK COVID-19 inquiry in November 2023, was invited It comments on the scientific and statistical literacy of the Johnson administration, including its namesake Johnson himself.
Vallance’s personal diary from the lockdown period, given as evidence to the committee, said Johnson was “clearly misled” by the flood of data and graphs his scientific advisers were giving him to understand the pandemic. Vallance said Johnson struggled with basic concepts of epidemiology (“I find it almost impossible to understand relative and absolute risks”).[He] I struggled with the whole concept of doubling. [times] …I couldn’t understand it) And overall, “ [him] I’m terrible at understanding statistics.”
The committee’s counsel (Mr. Connor) made a huge emphasis on the importance of Johnson’s skills gap, asking: “Mr. Johnson was, of course, a man making decisions that had very far-reaching effects on the whole country, so wasn’t it important that he understood the advice that was given?” Vallance answered, “Yes.” And it seems the UK was not the only country led by a scientifically illiterate man. Vallance said:
Well, I think the then Prime Minister was right to give up science at the age of 15. I think he would be the first to admit that science was not his forte and he struggled to understand some concepts and needed repeated repetition. And I remember a meeting with scientific advisors from all over Europe, and one of them – I won’t say which country – declared that the leader of that country had a huge problem with the exponential curve, and there was laughter throughout the call, because that was true in every country. So I don’t think it was necessarily unusual that the Prime Minister at the time didn’t understand some of these concepts, but it was sometimes a challenge to make sure that he understood what certain graphs and data were saying.
Even before COVID, Vallance (like Dominic Cummings, Boris Johnson’s former Svengali and key figure in UK COVID-19 policy) Already recorded He said he supports the UK government’s programme to boost its scientific and statistical capacity. Aftermath His testimony was joined by other voices, including the Royal Statistical Society (RSS), ahead of the recent general election. Manifesto And was sent letter Most of the leaders of the major political parties have called on their parties to ensure that their members and incoming ministers receive training in statistics and science. According to the RSS, this means making sure ministers know how to interpret the “data-based evidence” presented by scientific advisers and (to repeat Vallance’s statement that “…it is entirely appropriate for decision-makers to challenge the advice of science…”) what questions they should ask.
Vallance and the RSS’s proposal is reasonable in itself, given the nature of modern government. Michel Foucault and his successorsComplex scientific knowledge and the exercise of modern state power are intertwined like snakes coiling around a caduceus, the former enabling the latter by making new (or old) parts of the world legible, and the latter justifying the continued expansion and funding of the former. It is a real matter that actually formulating economic policy requires an understanding of somewhat lofty macroeconomic concepts like “aggregate demand,” “credit swap lines,” and “asset bubbles,” just as formulating public health policy during a pandemic requires some understanding of “exponential growth,” “doubling time,” and “absolute/relative risk.” Vallance’s revelations are thus not just embarrassing, but also suggest the limited capacity of our politicians to perform their duties of representing and effectively intervening in the world on behalf of our needs and interests.
However, their proposals were not sufficient and their failure to materialize was due to the opposition of the establishment (represented by Valence, the RSS and the Investigative Committee). Wide) A systemic misunderstanding of COVID-19 decision-making. They (i.e. the establishment) still see lockdowns as a legitimate policy approach and continue to draw the wrong lessons about the role of science in COVID-19 policy-making. For them, the Johnson government’s biggest error was not imposing lockdowns early or tough enough, and they believe this was driven by ministers’ failure to understand the science presented by epidemiologists and virologists, as epitomised by Vallance’s testimony. But if we see lockdowns as bringing about a startling and predictable catastrophe, then that that was And when we examine the decision-making processes that made this possible, it is clear that in the wake of COVID-19, politicians are not scientifically literate enough and we must demand that they become critics and even skeptics of science.
To be clear, I am not saying that politicians should be elected from among astrologers and chemtrail truthers (at least, it would be a stretch to describe these as “critical” or “skeptical”…) Rather, elected politicians should better understand the substance of scientific claims. To see why, consider briefly the dual nature of science and scientific ideas. actually How has it influenced the UK government’s COVID-19 policymaking?
Epidemiological concepts and modelling have made the world easier for ministers to understand, and many Government Scientists and colleague The transatlantic pandemic gave them the justification to impose large-scale lockdowns, but doing so raised epidemiological concerns. dominate The government’s decision-making process effectively sidelined concerns that are difficult, and impossible, to express in epidemiological terms. Lockdown policies were packaged and communicated with slogans like “lowering the R value,” “flattening the sombrero,” and “save the NHS,” all of which reflect variables in epidemiological models: “learning loss,” “loneliness,” and “the importance of normality” (the latter, incidentally, being a Before As pandemic influenza planning acknowledged, for a time we lived, breathed, and died according to an epidemiological model, with everything else treated as secondary or irrelevant.
We systematically ignore that scientific claims often involve normative judgements about what is important for human lives.
Now, there are many overlapping reasons why epidemiological modelling was given such importance, some institutional (e.g. many of the government’s most prominent advisers, including the Chief Medical Officer Chris Whitty, were epidemiologists) and some political (e.g. when presented with graphs showing the imminent “collapse” of the NHS, ministers could only focus on that). But underlying these there are also reasons we might describe as “ideological”, which have to do with the special importance given to scientific claims in the modern, post-Christian West. In his final posthumous book he wrote: Essay collectionPhysicist, philosopher of science, and professional gadfly Paul Feyerabend has pointed out that (1) we systematically ignore that scientific claims contain normative judgments about what matters for human life and practice, and (2) we tend to treat scientific claims as a unique or privileged source of information. Let me consider each briefly in turn.
Feyerabend argues that when science makes claims about realities “outside” the world, it is often in fact making claims about human lives and practices. should To take a farcical example of this, scientists like Richard Dawkins Challenge By combining religious claims about miracles and angelic visitations with scientific claims about what is actually real, they seek to wean people away from religious practice (although it is not clear that Feyerabend’s observations apply to all scientific claims). To tell (Scientists who claim that macroscopic objects like tables and chairs are “not real” don’t really expect us to start living according to that…). In the context of policy-making, scientists make a similar argument to Dawkins: by describing “what is out there”, they offer ministers an explanation of what in particular governments should focus on and organise their policy-making activity. Epidemiologists have provided ministers with a focus for policy by providing them with models that describe the world in terms of “reproduction numbers”, “death rates”, and “demand for ICU beds”.
But this alone does not explain why epidemiological modelling is needed to inform COVID-19 policy. Only To understand this, we need to go back to Feyerabend’s second observation: he tends to think of scientific knowledge in quasi-monadic terms, i.e. One Even science One Science is one type of true knowledge, so when science makes claims about a particular domain, we tend to act as if it is the most or only relevant source of information on that domain (as UK ministers did in 2020). Of course, this does not explain why epidemiology has triumphed over other sciences, such as economics (where epidemiology is the only science). I had already warned There is (probably) room to argue for a recession, but to do so we need to extend Feyerabend’s analysis further than his book. Looking back at the first months of 2020, it is clear that just as there is a hierarchy between scientific and non-scientific knowledge, there is also a hierarchy between highly quantitative, computer-model-based pro-lockdown arguments and more qualitative or historical arguments. As historian Toby Green puts it: I have written In 2021,
… It is noteworthy that the scientists who have been spearheading lockdown policies have been computer modellers. […] These models are attractive to governments, […] He was already fascinated by data-driven models of policymaking, and so the initial lockdown policies were born out of a preference for computer simulation models over the experience of medical history in treating a new pandemic.
Epidemiological concepts and models informed pandemic policy as follows: In particular(1) Explain to pastors what they should focus on, and (2) engage the minds of those who are already committed to the worship of science and data.
The impact of COVID-19 policy on ministers’ science education goes far beyond what is stated in the RSS letter. In addition to improved scientific and statistical literacy (which, as I say, is necessary for politicians to do their jobs), a kind of ideological shift is needed: a grand demystification of the scientific enterprise. Ministers certainly need to get better at reading and interpreting graphs, but they must also learn to see them as biased and leading, and as needing to be systematically balanced with other competing explanations of what matters. Pursuing the former without the latter runs the risk that ministers will only come to ever more uncritically accept scientists’ narrow worldviews and disciplinary priorities. To continue to reap the benefits of science-based policy and avoid its terrible harms, we need to see science as it is: useful and (often) fascinating, but reductive and (just as often) value-laden.
For your information, I would have voted for a party that promised something similar to this.
