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.