This is probably one of the most amazing studies on Scepticism and Sceptics (Covid, in this case) I’ve ever read, which just blows away Lew’s clumsy and faltering attempts to mischaracterise and traduce climate sceptics. It really is a work of fine art which comes to all the ‘wrong’ conclusions about a group of people who formally question the official scientific narrative. Having done so it then proceeds to completely turn those conclusions (which are glowingly positive overall) upon their head to bizarrely argue for a negative interpretation of scepticism which is totally unjustified by their findings! I’ve never seen anything like it.
Before I give my own analysis of the study, here are some tweets from a person equally amazed by it:
Astounding! Nobody ever wrote such a glowing ‘critical’ report on climate sceptics! As far as our detractors are concerned we are a bunch of retarded, anti-science, know-nothing deniers who have the audacity to challenge the ‘experts’ using crude denialist talking points, moon landing type conspiracy theories and graphs and data which have long been debunked by researchers and by reality itself. Mind you, there doesn’t appear to be a great deal of natural crossover between climate scepticism and Covid scepticism, a fact which has caused me considerable personal distress over the last year.
These researchers however, really took a deep dive into the Covid scepticism universe, perhaps expecting it to be inhabited by tin-foil hat wearing, unsophisticated, ill informed, scientifically illiterate numbskulls (maybe after they read Lew and Cook’s outpourings on climate scepticism), only to discover that it was populated by people who valued science and empirical data rather more than their ideological opponents and what is more, were often better qualified to analyse that data than their opponents! BIG lol.
I will just add to Commie Lee Jones’ series of excellent tweets with a few choice quotes from the paper of my own. This is particularly revealing:
Far from ignoring scientific evidence to argue for individual freedom, antimaskers often engage deeply with public datasets and make what we call “counter-visualizations”—visualizations using orthodox methods to make unorthodox arguments—to challenge mainstream narratives that the pandemic is urgent and ongoing.
This is a bizarre argument. What they are saying in effect is that natural conclusions from the data are unorthodox, whereas the unsubstantiated and demonstrably illogical conclusions of policy makers and government science advisers, using the same data, is to be considered orthodox. You see what they did? Lockdowns and mass mask wearing, never before used to try to control a pandemic (with the exception of Spanish ‘flu patchily implemented mask mandates in 1918 – which demonstrably failed) are now orthodox. Natural, logical and scientific interpretations of the data are now unorthodox.
However, we find that anti-mask groups on Twitter often create polished counter-visualizations that would not be out of place in scientific papers, health department reports, and publications like the Financial Times.
While previous literature in visualization and science communication has emphasized the need for data and media literacy as a way to combat misinformation [43, 47, 89], this study finds that anti-mask groups practice a form of data literacy in spades. Within this constituency, unorthodox viewpoints do not result from a deficiency of data literacy; sophisticated practices of data literacy are a means of consolidating and promulgating views that fly in the face of scientific orthodoxy.
So, they find that “anti-mask groups practice a form of data literacy in spades”. Hilarious!
The following passage reveals that the authors do not in fact understand what science actually is, as they equate ‘mainstream science’ with the prevailing public narrative.
In media studies, the term “counterpublic” describes constituencies that organize themselves in opposition to mainstream civic discourse, often by agentively using communications media . In approaching anti-maskers as a counterpublic (a group shaped by its hostile stance toward mainstream science), we focus particular attention on one form of agentive media production central to their movement: data visualization. We define this counterpublic’s visualization practices as “counter-visualizations” that use orthodox scientific methods to make unorthodox arguments, beyond the pale of the scientific establishment.
I think the authors must be media studies graduates by the sound of it. ‘Mainstream civic discourse’ is not mainstream science and conclusions based on the use of orthodox scientific methods are not, by definition, beyond the pale of the scientific establishment. What an utterly ridiculous thing to say.
Here they go again, mistaking a mythical Covid ‘scientific consensus’ for mainstream epidemilogical science when it is nothing of the sort. There is no consensus on Covid beyond an inflexible, rigidly enforced, medically unprecedented and globally homogeneous political response to the pandemic allegedly scientifically informed by a very few ‘expert’ modelers and even fewer epidemiologists. The authors do not understand this at all. Hence they equate rational, science-based questioning of the prevailing political and social narrative with a political counter culture.
As a subculture, anti-masking amplifies anti-establishment currents pervasive in U.S. political culture. Data literacy, for antimaskers, exemplifies distinctly American ideals of intellectual selfreliance, which historically takes the form of rejecting experts and other elites . The counter-visualizations that they produce and circulate not only challenge scientific consensus, but they also assert the value of independence in a society that they believe promotes an overall de-skilling and dumbing-down of the population for the sake of more effective social control.
The authors double down on their confused idea of what science is and by so doing they increasingly mischaracterize so called ‘anti-maskers’ who rely upon science and data to question the alleged ‘scientific consensus’ on Covid, a consensus which does not exist and a dominant narrative which is most definitely not rooted firmly in established science.
While academic science is traditionally a system for producing knowledge within a laboratory, validating it through peer review, and sharing results within subsidiary communities, anti-maskers reject this hierarchical social model. They espouse a vision of science that is radically egalitarian and individualist. This study forces us to see that coronavirus skeptics champion science as a personal practice that prizes rationality and autonomy; for them, it is not a body of knowledge certified by an institution of experts.
Finally, what is most revealing is that these authors haven’t got a clue why the ‘antimaskers’ come to such divergent conclusions from the supposed ‘mainstream’ using exactly the same data. They just waffle some nonsense about cases and deaths in an attempt to explain it – and fail, miserably:
So how do these groups diverge from scientific orthodoxy if they are using the same data? We have identified a few sleights of hand that contribute to the broader epistemological crisis we identify between these groups and the majority of scientific researchers. For instance, they argue that there is an outsized emphasis on deaths versus cases: if the current datasets are fundamentally subjective and prone to manipulation (e.g., increased levels of faulty testing, asymptomatic vs. symptomatic cases), then deaths are the only reliable markers of the pandemic’s severity. Even then, these groups believe that deaths are an additionally problematic category because doctors are using a COVID diagnosis as the main cause of death (i.e., people who die because of COVID) when in reality there are other factors at play (i.e., dying with but not because of COVID). Since these categories are fundamentally subject to human interpretation, especially by those who have a vested interest in reporting as many COVID deaths as possible, these numbers are vastly over-reported, unreliable, and no more significant than the flu.
To underline the fact that they haven’t got a clue, they say this, near the end of the paper:
Understanding how these groups skillfully manipulate data to undermine mainstream science requires us to adjust the theoretical assumptions in HCI research about how data can be leveraged in public discourse.
By not having the foggiest idea how Covid sceptics arrive at conclusions so very different from the alleged ‘consensus’, the authors simply revert to accusing them of ‘skillfully manipulating’ the data in order to ‘undermine mainstream science’. So actually, in conclusion, although this study gives credit where credit is due to Covid sceptics, their overall approach is not so very different from Lewandowsky et al after all.
Update: The Conservative Woman “Covid sceptics aren’t as stupid as we thought, say experts”
SCIENCE is good. The use of the scientific method was first found in Babylonian texts and was filtered through the mind of Aristotle. It travelled, gaining definition and seriousness, via Arab physicists and the Somerset monk and Oxford scholar Roger Bacon. From there it bounced through the minds of Galileo, Descartes and Newton until finally becoming codified and universally accepted as: (1) observation and experiment, (2) hypothesis, (3) verification by fresh observation and experiment.
The government today claims that it is led by data, not dates. The government’s policies on lockdown and Covid are not political but strictly ‘based on the science’. Government information films are fronted by scientific high priests. Never in the history of the UK has public policy been so outsourced to the men and women in lab coats.
Ranged against them are the rag-tag, amateur and by definition ignorant ranks of the lockdown sceptics, baffled by numbers and complaining about ongoing restrictions in the face of mutations and variants.
Researchers at MIT set out to find out the way that US lockdown sceptics, and in particular mask sceptics, were using data, what data they were using and what primary colours they were using for their fingerpaints.
Something I missed which has an immediate resonance with Climate ‘why should we give you our data if you are going to use it against us?’ Gate:
So much so that at one point the MIT team suggest that far from allowing greater public access to the data, the data should be made more difficult to find:
‘These findings suggest that the ability for the scientific community and public health departments to better convey the urgency of the US coronavirus pandemic may not be strengthened by introducing more downloadable datasets . . .
What the MIT team has discovered is not what was assumed, mostly by government-supporting scientists, that the general public were, ‘data illiterate’: far from it. Allowing them unhindered access to the data, instead of undermining lockdown-sceptics, strengthens their hand. They sound baffled by the sceptics who ‘often reveal themselves to be more sophisticated in their understanding of how scientific knowledge is socially constructed than their ideological adversaries, who espouse naïve realism about the “objective” truth of public health data’.
And here is the Lew roll/Merchants of Doubt moment right at the end of the paper, when the authors just cannot reconcile their findings with their confirmational bias, so resort to simple name-calling and insults:
Then comes the pay-off. For some reason the MIT researchers, obviously so disgusted by finding out that ordinary people are rigorous and not nearly as stupid as generally thought, compare them to the tobacco lobby and the January Capitol Hill protesters.
By engaging in such wild and unreasonable ad hominems they merely look as if they are trying to be acceptable in the MIT common room, despite their findings. Those findings are clear that if anybody is applying the traditional idea behind the scientific method, it is not those supporting the Government’s approach to lockdown policy, but those questioning it.