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The death of science?
Last week’s post began my three part series examining the corrosive effects of the politicisation of science. If you haven’t already done so, please read it now because I’m going to dive straight back into this topic with…
‘Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online‘, a paper that was originally presented at a conference on ‘human factors in computing systems’, is currently hosted on arXiv*.
The paper is essentially an analysis of the beliefs and practices of individuals and groups who have used social media to disseminate data visualisations (such as charts, graphs and annotated maps) in order to promote critical thinking on the approach that has been taken to COVID-19 by federal, state and local health authorities - an approach which, although the authors of the paper do not admit it, deviates sharply from all previous evidence-based public health advice on managing respiratory epidemics.
The authors analysed Twitter posts produced by these individuals, whom they branded “coronavirus skeptics” – with a nod to the pejorative term “climate skeptics” – and “anti-maskers”, and ‘infiltrated’ five Facebook groups in which such people congregated in order to share data, learn how to obtain accurate data, refine their understanding of these data, and develop greater skills in representing data in a graphical form that could be easily understood by members of the public who were not experts in data analysis.
Despite its apparently dry subject matter, the paper makes for interesting reading. On the one hand, the authors acknowledge the strong commitment to data integrity, accuracy in data representation, acknowledgement of personal bias, and openness to new insights and understandings of data which were displayed by the people and groups they studied. (In other words, their research subjects behaved like scientists.)
They describe in detail the lengths to which the “anti-maskers” go to obtain up-to-date and complete data; the commitment to helping other members of these groups to obtain, understand and graphically represent data in an accurate way; the vigorous debates within groups about the accuracy and usefulness of the data they’ve obtained and possible ways these might be improved; the high standards maintained by groups on sharing data visualisations; the high quality of data visualisations that they produce; and the encouragement to openly acknowledgement personal bias in an attempt to overcome its distorting effects on data interpretation.
Here are some illustrative quotes from the paper:
“This study finds that anti-mask groups practice a form of data literacy in spades.”
“Users provide numerous tutorials on how to access government health data. These tutorials come either as written posts or as live screencasts, where a user (often a group administrator or moderator) demonstrates the process of downloading information from an open data portal.”
“Even as these users learn from each other to collect more data, they remain critical about the circumstances under which the data are collected and distributed. Many of the users believe that the most important metrics are missing from government-released data.”
“These individuals as a whole are extremely willing to help others who have trouble interpreting graphs with multiple forms of clarification: by helping people find the original sources so that they can replicate the analysis themselves, by referencing other reputable studies that come to the same conclusions, by reminding others to remain vigilant about the limitations of the data, and by answering questions about the implications of a specific graph.”
“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.”
“Among other initiatives, these groups argue for open access to government data (claiming that CDC and local health departments are not releasing enough data for citizens to make informed decisions).”
“While users contend that their data visualizations objectively illustrate how the pandemic is no worse than the flu, they are similarly mindful to note that these analyses only represent partial perspectives that are subject to individual context and interpretation.“I’ve never claimed to have no bias. Of course I am biased, I’m human,” says one prolific producer of anti-mask data visualizations. “That’s why scientists use controls…to protect ourselves from our own biases. And this is one of the reasons why I disclose my biases to you. That way you can evaluate my conclusions in context. Hopefully, by staying close to the data, we keep the effect of bias to a minimum”(August 14, 2020). They are ultimately mindful of the subjectivity of human interpretation, which leads them to analyzing the data for themselves.”
“Both [Professor Michael] Levitt and these anti-mask groups identify the dangerous convergence of science and politics as one of the main barriers to a more reasonable and successful pandemic response, and they construct their own data visualizations as a way to combat what they see as health misinformation. ‘To be clear. I am not downplaying the COVID epidemic,’ said one user. ‘I have never denied it was real. Instead, I’ve been modeling it since it began in Wuhan, then in Europe, etc.[…] What I have done is follow the data. I’ve learned that governments, that work for us, are too often deliberately less than transparent when it comes to reporting about the epidemic’ (July 17, 2020). For these anti-mask users, their approach to the pandemic is grounded in a [sic] more scientific rigor, not less.”
The authors also acknowledge that the efforts of these data-driven individuals and groups have had beneficial real-world effects on ensuring data integrity:
“In Texas, a coalition of mayors, school board members, and city council people investigated the state’s COVID-19 statistics and discovered that a backlog of unaudited tests was distorting the data, prompting Texas officials to employ a forensic data team to investigate the surge in positive test rates.”
In other words, the authors of the paper acknowledge that the people they’re studying have formed a “community of practice… focused on acquiring and transmitting expertise”.
However, rather than lauding the commitment of the “anti-maskers” to upholding the practices of the scientific method, and the values underpinning it, the authors express deep concern that they are “us[ing] orthodox scientific methods to make unorthodox arguments, beyond the pale of the scientific establishment.”
In other words, they do not dismiss the subjects of their investigation as “anti-science”. Instead, they betray their own ignorance of what science is, by accusing “anti-maskers” of utilising the scientific method not only to deviate from “scientific orthodoxy” – an oxymoron if ever there was one – but to empower individuals and mobilise community support for changes in public health policy that the authors deem dangerous:
“This paper investigates how these activist networks use rhetorics of scientific rigor to oppose these public health measures. Far from ignoring scientific evidence to argue for individual freedom, anti-maskers 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. By asking community members to ‘follow the data,’ these groups mobilize data visualizations to support significant local changes.”
“These groups leverage the language of scientific rigor — being critical about data sources, explicitly stating analytical limitations of specific models, and more — in order to support ending public health restrictions despite the consensus of the scientific establishment.”
“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. Not only are these groups prolific in their creation of counter-visualizations, but they leverage data and their visual representations to advocate for and enact policy changes on the city, county, and state levels.”
“These communities use data analysis as a way to socialize and enculturate their users; they promulgate data literacy practices as a way of inculcating heterodox ideology. The transmission of data literacy, then, becomes a method of olitical radicalization.”
“While these groups highly value scientific expertise, they also see collective analysis of data as a way to bring communities together within a time of crisis, and being able to transparently and dispassionately analyze the data is crucial for democratic governance. In fact, the explicit motivation for many of these followers is to find information so that they can make the best decisions for their families—and by extension, for the communities around them.”
“People who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes”
Whereas, as I pointed out in Part 1, science is by its very nature an endlessly iterative process of repeating the steps of the scientific method in order to more and more closely approximate certainty, while acknowledging the near-impossibility of absolute certainty, the authors of the paper express consternation that
“Most fundamentally, the groups we studied believe that science is a process, and not an institution.”
I re-read this particular sentence several times, to make sure I hadn’t misunderstood its meaning. But there can be no doubt about it: the authors of this paper are actually arguing that science is an institution (“orthodox science”), rather than a process.
Furthermore, those who refuse to stay within the tightly-constrained boundaries of this institution, by utilising the scientific method to independently “understand and analyze the information for themselves, free from biased, external intervention”, are to be reviled and feared:
“Anti-mask groups mobilize visualizations politically to achieve powerful and often horrifying ends.”
What should be done, then, about these individuals and groups who insist on using the methodology of science to “defy[…] public health officials”rather than meekly obeying the diktats of those who are anointed as the “scientific establishment”?
The authors caution against greater data transparency, arguing that it will not close – and may indeed widen – the “epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data”:
“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, by producing “better visualizations” (e.g., graphics that are more intuitive or efficient), or by educating people on how to better interpret them. This study shows that there is a fundamental epistemological conflict between maskers and anti-maskers, who use the same data but come to such different conclusions. As science and technology studies (STS) scholars have shown, data is not a neutral substrate that can be used for good or for ill… Indeed, anti-maskers often reveal themselves to be more sophisticated in their understanding of how scientific knowledge is socially constructed than their ideological adversaries, who espouse naive realism about the ‘objective’ truth of public health data.”
Or, in plain English, “anti-maskers” have a very different worldview than those who identify with “scientific orthodoxy”, and providing them with more data might only make them more dangerous to that orthodoxy by equipping them with better tools to call into question the evidence base for, and the proportionality of, COVID-related public health policy.
The authors of the paper recognise that “anti-maskers” are suspicious of the way that science, and scientists, have been recruited by those in power to legitimise their authority over citizens:
“Like data feminists, anti-mask groups similarly identify problems of political power within datasets that are released (or otherwise withheld) by the US government. Indeed, they contend that the way COVID data is currently being collected is non-neutral, and they seek liberation from what they see as an increasingly authoritarian state that weaponizes science to exacerbate persistent and asymmetric power relations.”
However, their response to this critique is to argue against the intellectual empowerment of citizens, because it may foment dangerous challenges to authority:
“Calling for increased media literacy can often backfire: the instruction to ‘question more’ can lead to a weaponization of critical thinking and increased distrust of media and government institutions.”
In other words, it doesn’t pay to encourage the stupid rubes to think for themselves, because if they do so, they might start objecting to being propagandised by the corporate-controlled media and bossed around by power-crazed politicians and bureaucrats. And we couldn’t have that, could we?
No, rather than educating the public and encouraging them to engage in rational debate about policies that profoundly affect their lives, the authors of the paper instead essentially advise smearing those who seek to do so with pejorative labels such as "anti-masker" and surreptitiously associating them with morally repugnant ideologies such as "white supremacy", marginalised political movements, or risible positions such as rejection of evolution, presumably so that the public will dismiss them:
"Calls for media literacy—especially as an ethics smokescreen to avoid talking about larger structural problems like white supremacy—are problematic when these approaches are deficit-focused and trained primarily on individual responsibility."
"For Tea Party activists, this deep story revolved around anger towards a federal system ruled by liberal elites who pander to the interests of ethnic and religious minorities, while curtailing the advantages that White, Christian traditionalists view as their American birthright. We argue that the anti-maskers’ deep story draws from similar wells of resentment, but adds a particular emphasis on the usurpation of scientific knowledge by a paternalistic, condescending elite that expects intellectual subservience rather than critical thinking from the lay public."
"Moreover, their simultaneous appropriation of scientific rhetoric and rejection of scientific authority also reflects longstanding strategies of Christian fundamentalists seeking to challenge the secularist threat of evolutionary biology".
So, after remarking upon the commitment to good scientific practice demonstrated by the subjects of their study, the authors - incomprehensibly and without presenting a single shred of evidence - lump them in with neonazis and evolution denialists. And they managed to get their findings presented at a scientific conference!
All but one of the authors of this article hail from the famed Massachusetts Institute of Technology (MIT), currently ranked among the most prestigious academic institutions in the world. Since its founding in 1861, MIT has played a key role in the development of modern science, engineering, mathematics, and technology.
Such developments, as is widely acknowledged, are generally driven by individuals who challenge scientific orthodoxy. The authors even allude to the vital role played by the “heterodox” scientist by commenting on the fact that “anti-mask groups point to Thomas Kuhn’s The Structure of Scientific Revolutions [a book that illustrates how the “scientific orthodoxy” that the authors of this paper celebrate is in fact a hindrance to scientific progress] to show how their anomalous evidence — once dismissed by the scientific establishment — will pave the way to a new paradigm”.
One wonders how previous graduates of this illustrious institution, such as the paradigm-smashing physicist Richard Feynman, would react to a paper that decries the use of the scientific method by curious, well-qualified and data-literate individuals who are seeking to gain a better understanding of their world, and share this understanding with others.
If, Matthew Crawford puts it, “‘science’ stands for a freedom of the mind that is inherently at odds with the idea of authority”, then science is dying, and this paper is a giant tombstone in its graveyard. Those who have assumed authority over you do not believe that you should be allowed to think for yourself, as the philosophy of science enjoins you to do, and the authors of this aper are willing handmaidens to that authority regardless of its legitimacy.
Now, more than ever, it is critical for every person to acquire and practise the tools and skills of intellectual self-defence. It’s no exaggeration to state that your way of life, and even your life itself, may depend on it.
* arXiv (pronounced ‘archive‘) is a pre-print server for scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Pre-print servers allow researchers to share articles that have not yet been accepted for publication in peer-reviewed journals – a process that can take months or even years – with the international scientific community.