MIT Study: “For these anti-mask users, their approach to the pandemic is grounded in a more scientific rigor, not less.” (Parts 1+2)
“Most fundamentally, the groups we studied believe that science is a process, and not an institution.”
Video Version (In Multiple Uploads)
Full Video (151 min)
Part 1: Reactions & Commentary (37 min)
Part 2: Conclusions After Research (10 min)
Appendix A: Quotes & Notes from Mask Studies (55 min)
Appendix B: Summaries of Mask Studies (5 min)
Appendix C: A Familiar Meme (3 min)
[Trailer] MIT study finds anti-maskers are “grounded in a more scientific rigor”
4 min: https://tinyurl.com/4n9445vj
[FULL Reactions+Research] MIT study finds anti-maskers are “grounded in a more scientific rigor”
151 min: https://tinyurl.com/ur3s8uuh
[Reactions+Research Conclusions] (Parts 1+2)
48 min: https://tinyurl.com/r8t59ypk
[Research Conclusions] (Part 2)
11 min: https://tinyurl.com/rn6ffsd2
[Research Notes] (Appendix A+B)
61 min: https://tinyurl.com/kvk8d467
[Familiar CDC Meme] (Appendix C)
3 min: https://tinyurl.com/pke3atbk
Full Essay
Introduction — Discussing MIT Study — Mask Evidence Research Conclusions — Appendix A: Research Quotes & Notes — Appendix B: Summaries of References Reviewed — Appendix C: A Familiar Meme — Corrections
Introduction
“Most fundamentally, the groups we studied believe that science is a process, and not an institution.”
A team from MIT put together a fascinating study on online research communities. I loved reading it and wanted to share with others to help them try to understand my world. Some of the research communities they studied sounded adjacent to independent research and media communities that I have participated in since before the 2009 Swine Flu Pandemic. This study also provides some fascinating lenses on my work in progress at buckystats.org.
As a software engineer who wants collective decisions to be more evidence-based, especially wherever informed consent is likely to be violated, this study was very fun to read. I only have a B.S. from the University of Maryland in 2005 for Computer Science with a concentration in Mathematics, through the Science, Technology, & Society Scholars program. I’ve been a public health activist since age nine with my first decade focused on big tobacco’s targeting of children. My second decade focused on ending the devastating war on drugs, where public health issues continue to be unjustly treated as criminal issues.
I do not claim this is a rigorous scientific inquiry, but rather, a rigorous dive into the resources and rhetoric cited in the 2021 MIT study first brought to my attention by The Last American Vagabond. Thanks you for all your work, Ryan!
This article began as a subjective reaction to the MIT study, written to share with others to help them understand. But as I began digging into the literature cited, it grew into a more objective review on the sub-topic of evidence for the efficacy of universal mask mandates.
Please find below the 2021 MIT study titled “Viral Visualization: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online.” I have pulled quotes in sequential order and included page numbers from the study for you to reference. Throughout this article, I pivot from the study to my own running analysis throughout, and I welcome your engagement and discussion.
Discussing The New MIT Study
Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online (May 2021, PDF, Explainer Video, Interactive)
https://dl.acm.org/doi/10.1145/3411764.3445211
Epistemological Rifts
“ABSTRACT: Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government’s pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that 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. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.”
PAGE 1
Who is advocating for radical policy changes? From my perspective, a year of universal community mask mandates is a very radical policy change. “Anti-maskers” simply support the pre-2020 established social norms as well as the pre-2020 scientific paradigm regarding potential mask mandate efficacy. But the MIT study dives in…
“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. 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.
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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. Data visualizations are not a neutral window onto an observer-independent reality; during a pandemic, they are an arena of political struggle.
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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 [14, 46, 84]. 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. Quantitative data is culturally and historically situated; the manner in which it is collected, analyzed, and interpreted reflects a deeper narrative that is bolstered by the collective effervescence found within social media communities. Put differently, there is no such thing as dispassionate or objective data analysis. Instead, there are stories: stories shaped by cultural logics, animated by personal experience, and entrenched by collective action. This story is about how a public health crisis—refracted through seemingly objective numbers and data visualizations—is part of a broader battleground about scientific epistemology and democracy in modern American life.
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There is a temptation in studies of this nature to describe these groups as “anti-science,” but this would make it completely impossible for us to meaningfully investigate this article’s central question: understanding what these groups mean when they say “science.”
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By comparing the visualizations shared within anti-mask and mainstream networks, we discover that there is no significant difference in the kinds of visualizations that the communities on Twitter are using to make drastically different arguments about coronavirus (figure 3). Antimaskers (the community with the highest percentage of verified users) also share the second-highest number of charts across the top six communities (table 1), are the most prolific producers of area/line charts, and share the fewest number of photos (memes and images of politicians; see figure 3).
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This leads us to an interpretive question that animates the Facebook analysis: how can opposing groups of people use similar methods of visualization and reach such different interpretations of the data? We approach this problem by ethnographically studying interactions within a community of anti-maskers on Facebook to better understand their practices of knowledge-making and data analysis, and we show how these discussions exemplify a fundamental epistemological rift about how knowledge about the coronavirus pandemic should be made, interpreted, and shared.”PAGE 2-5
Yes, very well done! One fundamental epistemological rift could separate people who no longer take institutional data and interpretation at face value or as unquestionable gospel. A subset of that rift might only feel that way regarding totally novel situations with totally inadequate data. Another rift might only find scientific experiments compelling if they have a control arm. Another rift might simply consider the successful replication of experiments to be a minimum prerequisite to building “evidence-based” public policy. Many of us are just pointing out the extreme levels of uncertainty, and declaring that we do not consider the data and/or logic as very actionable — especially not for life-and-death decisions.
Another epistemological rift is happy to make public policy based on a couple of novel studies which “suggest” something “might” have some effect. A subset of them do not need to see any studies on the potential consequences of the policy. But to my mind, heart, and gut… in many cases, the best conclusions are still far too uncertain to ethically threaten people with fines, jail, and/or a state-sanctioned demolition of their small businesses, livelihoods, or personal passions.
It seems the most publicized public health experts are more than happy to support public policies that have as much peer-reviewed evidence as a click-bait headline.
“These statistics suggest that anti-maskers tend to be among the most prolific sharers of data visualizations on Twitter, and that they overwhelmingly amplify these visualizations to other users within their network (88.97% of all retweets are in-network).”
PAGE 10
I don’t know if Twitter was shadow banning people this past year, as often occurs in Facebook and YouTube algorithms. But countless posts were flagged as potential misinformation, and user accounts were deleted. This further compacted the antimasker network’s footprint remaining on each platform last year. This might be an unacknowledged factor in the high percentage.
“For these anti-mask users, their approach to the pandemic is grounded in a more scientific rigor, not less.
4.2.6 Developing expertise and processes of critical engagement.
The goal of many of these groups is ultimately to develop a network of well-informed citizens engaged in analyzing data in order to make measured decisions during a global pandemic.
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The discussion-based nature of these Facebook groups also give these followers a space to learn and adapt from others, and to develop processes of critical engagement.
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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. The last point is especially salient, as it surfaces both what these groups see as a reliable measure of how the pandemic is unfolding and what they believe they should do with the data. These online communities therefore act as a sounding board for thinking about how best to effectively mobilize the data towards more measured policies like slowly reopening schools. “You can tell which places are actually having flare-ups and which ones aren’t,” one user writes. “Data makes us calm.” (July 21, 2020)
Additionally, followers in these groups also use data analysis as a way of bolstering social unity and creating a community of practice. 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.
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The message that runs through these threads is unequivocal: that data is the only way to set fear-bound politicians straight, and using better data is a surefire way towards creating a safer community.
DISCUSSION
Anti-maskers have deftly used social media to constitute a cultural and discursive arena devoted to addressing the pandemic and its fallout through practices of data literacy. Data literacy is a quintessential criterion for membership within the community they have created. The prestige of both individual anti-maskers and the larger Facebook groups to which they belong is tied to displays of skill in accessing, interpreting, critiquing, and visualizing data, as well as the pro-social willingness to share those skills with other interested parties. This is a community of practice [63, 102] focused on acquiring and transmitting expertise, and on translating that expertise into concrete political action. Moreover, this is a subculture shaped by mistrust of established authorities and orthodox scientific viewpoints. Its members value individual initiative and ingenuity, trusting scientific analysis only insofar as they can replicate it themselves by accessing and manipulating the data firsthand. They are highly reflexive about the inherently biased nature of any analysis, and resent what they view as the arrogant self-righteousness of scientific elites.
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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 [39, 52, 98]. As they see it, to counter-visualize is to engage in an act of resistance against the stifling influence of central government, big business, and liberal academia.
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Most fundamentally, the groups we studied believe that science is a process, and not an institution. As we have outlined in the case study, these groups mistrust the scientific establishment (“Science”) because they believe that the institution has been corrupted by profit motives and politics. The knowledge that the CDC and academics have created cannot be trusted because they need to be subject to increased doubt, and not accepted as consensus. In the same way that climate change skeptics have appealed to Karl Popper’s theory of falsification to show why climate science needs to be subjected to continuous scrutiny in order to be valid [42], we have found that anti-mask groups point to Thomas Kuhn’s The Structure of Scientific Revolutions to show how their anomalous evidence— once dismissed by the scientific establishment—will pave the way to a new paradigm … For anti-maskers, valid science must be a process they can critically engage for themselves in an unmediated way. Increased doubt, not consensus, is the marker of scientific certitude.
Arguing that anti-maskers simply need more scientific literacy is to characterize their approach as uninformed and inexplicably extreme. This study shows the opposite: users in these communities are deeply invested in forms of critique and knowledge production that they recognize as markers of scientific expertise. If anything, anti-mask science has extended the traditional tools of data analysis by taking up the theoretical mantle of recent critical studies of visualization [31, 35]. Anti-mask approaches acknowledge the subjectivity of how datasets are constructed, attempt to reconcile the data with lived experience, and these groups seek to make the process of understanding data as transparent as possible in order to challenge the powers that be.
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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.
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Convincing anti-maskers to support public health measures in the age of COVID-19 will require more than “better” visualizations, data literacy campaigns, or increased public access to data. Rather, it requires a sustained engagement with the social world of visualizations and the people who make or interpret them.“PAGE 12-15
(More on dumbing us down: The Ultimate History Lesson)
With any novel topic explored by science, there should not be any public ‘consensus’ about scientific findings yet. That itself is already ‘anti-science’. Knowledge on new topics is constantly in flux and we’ve barely identified the first batch of known unknowns related to most of 2020.
This is part of the epistemological rift: many seem content to quickly believe a brand new scientific paradigm and consensus which cannot possibly have enough verified evidence to support it yet. It seems one of the biggest flaws in this study is its assumption that “anti-maskers” are indeed generally wrong and have an “unorthodox” analysis of “a preponderance of evidence.” But in reality, most evidence suggests little-to-no effect.
Unorthodox Positions Citing Orthodox Institutions
Nonpharmaceutical Interventions (NPIs)
Public health studies specific to SARS-CoV-2 only emerged in 2020. So most previous studies focused on influenza-like illnesses, and occasionally SARS or MERS. At the start of this pandemic — and declarations of consensus of “the science” — there were decades of studies regarding masks and influenza-like illnesses. (In Appendix A, I poke the most obvious holes in the experimental designs of almost every new study on SARS-CoV-2, as I see them.)
This January 2021, I found this guidance that the World Health Organization (WHO) published in October 2019 titled, “Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza.” (PDF) The ‘available evidence’ section of the executive summary states that:
“… the evidence base on the effectiveness of NPIs in community settings is limited, and the overall quality of evidence was very low for most interventions. There have been a number of high quality randomized controlled trials (RCTs) demonstrating that personal protective measures such as hand hygiene and face masks have, at best, a small effect on influenza transmission, although higher compliance in a severe pandemic might improve effectiveness. However, there are few RCTs for other NPIs, and much of the evidence base is from observational studies and computer simulations.”
PAGE 8
“There is also a lack of evidence for the effectiveness of improved respiratory etiquette and the use of face masks in community settings during influenza epidemics and pandemics.”
PAGE 10
“Ten relevant RCTs were identified for this review and meta-analysis to quantify the efficacy of community-based use of face masks, including more than 6000 participants in total (42-47, 50, 68-70). Most trials combined face masks with improved hand hygiene, and examined the use of face masks in infected individuals (source control) and in susceptible individuals.
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Ten RCTs were included in meta-analysis, and there was no evidence that face masks are effective in reducing transmission of laboratory-confirmed influenza.“
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In May 2020, I found the Centers for Disease Control and Prevention’s (CDC) policy review published earlier in May titled, “Nonpharmaceutical Measures for Pandemic Influenza in Nonhealthcare Settings—Personal Protective and Environmental Measures.” The abstract states:
“Although mechanistic studies support the potential effect of hand hygiene or face masks, evidence from 14 randomized controlled trials of these measures did not support a substantial effect on transmission of laboratory-confirmed influenza. We similarly found limited evidence on the effectiveness of improved hygiene and environmental cleaning. We identified several major knowledge gaps requiring further research, most fundamentally an improved characterization of the modes of person-to-person transmission.”
The CDC’s methods and results section explains:
“In our systematic review, we identified 10 RCTs that reported estimates of the effectiveness of face masks in reducing laboratory-confirmed influenza virus infections in the community from literature published during 1946–July 27, 2018. In pooled analysis, we found no significant reduction in influenza transmission with the use of face masks (RR 0.78, 95% CI 0.51–1.20; I2 = 30%, p = 0.25) (Figure 2).
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Most studies were underpowered because of limited sample size, and some studies also reported suboptimal adherence in the face mask group.
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There is limited evidence for their effectiveness in preventing influenza virus transmission either when worn by the infected person for source control or when worn by uninfected persons to reduce exposure. Our systematic review found no significant effect of face masks on transmission of laboratory-confirmed influenza.“
The CDC’s discussion section reiterates:
“In this review, we did not find evidence to support a protective effect of personal protective measures or environmental measures in reducing influenza transmission. Although these measures have mechanistic support based on our knowledge of how influenza is transmitted from person to person, randomized trials of hand hygiene and face masks have not demonstrated protection against laboratory-confirmed influenza, with 1 exception (18).
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We did not find evidence that surgical-type face masks are effective in reducing laboratory-confirmed influenza transmission, either when worn by infected persons (source control) or by persons in the general community to reduce their susceptibility (Figure 2).”
These two systematic reviews by the WHO in October 2019 and the CDC in May 2020 clearly confirmed that at the beginning of this pandemic we did not have compelling evidence to support the unprecedented public health policies. At the same time, an unquestionable ‘consensus’ had already been declared through the birth of the Post-2020 paradigms.
To empathize more with minds like mine…
Imagine reading in CDC’s May policy review that “14 randomized controlled trials of these measures did not support a substantial effect on transmission of laboratory-confirmed influenza,” and then feeling gaslit and censored for a straight year by the CDC.
Moving Forward, Together
Moving forward, why would we the [skeptical] people trust the judgment of those claiming consensus before there even existed compelling evidence? How many deceptions must we tolerate ‘for national security,’ with past violations compounding instead of justice being restored? It’s really a sick joke in a sad clown world, and given how little control we have over it, it’s usually more fun to laugh than cry about it… in between reading the documents and data, and tending the garden!
The CDC and WHO were speaking out of both sides of their overlapping mouths, recommending public policies for which they found little-to-no evidence. As acknowledged in this MIT study, Anthony Fauci admitted in June that he [misled or] lied to the public in March 2020 for reasons not disclosed to the public until later. (This was a compartmentalized intelligence operation.) He says the circumstances changed — but he’s talking about the mask supply, not the science. All of this directly contributed to a further implosion of trust in institutions that require pre-existing trust to be most effective. The fog of war creates such extreme problems, civilian causalities, and profit incentives. So declaring wars is rarely an effective or moral solution for anything.
“We’re at war. In a true sense, we’re at war, and we’re fighting an invisible enemy.”
“Look, we know what we need to do to beat this virus: Tell the truth. Follow the scientists and the science. Work together. Put trust and faith in our government to fulfill its most important function, which is protecting the American people — no function more important.
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That’s why I’m using every power I have as President of the United States to put us on a war footing to get the job done. It sounds like hyperbole, but I mean it: a war footing.”
“Either you are with us, or you are with the terorrists.”
“We must speak the truth about terror. Let us never tolerate outrageous conspiracy theories.”
Given that universal community mask mandates are a new and sudden scientific paradigm, how and when exactly was that debate “ended”? What was the trick used to turn “no significant effect” into “a preponderance of evidence” supporting an effect?
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First, the institutions say that laypeople cannot effectively understand scientific concepts and literature. Even though these topics have taken over almost all political decisions, we must outsource almost all of our thinking to “trust the experts.” Established institutions try to ensure that we only hear from their favorite <20% of credentialed experts. And they close the trap by declaring a consensus before compelling data had been collected, let alone experiments replicated and uncertainties verified.
(When the U.S. government used new data models last spring, some claim: “The data projections shared were neither peer-reviewed, nor submitted to the Federal Register to initiate a 60-day public comment period as required by law. As a result, the OMB was not able to approve the use of these projections, which makes their use by any federal agency, for any reason, illegal.“)
Better visualizations and sustained engagement will never replace a void in actionable data used to strong-arm a population. But I would be ecstatic for establishment ‘consensus’ institutions to directly engage with the ‘skeptics.’ Half the time, that would be enough to feel heard. We and our favorite credentialed experts are increasingly censored and/or targeted with mass-media smear campaigns. Fact-checking efforts rarely check more than straw men arguments.
Instead, we should try putting some of the strongest ‘skeptic’ experts up against some of the strongest institutionally favored experts in a series of long-form debates. Despite its limited Overton window, I’ve enjoyed the model of Intelligence Squared U.S. for over a decade. When strong data points collapse given more compelling evidence, any critical thinkers trying to increase their certainty will quickly abandon those positions.
“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.”
PAGE 15
From the perspective of my favorite research communities, this point is also not quite right. Peer review studies can be great, and we frequently cite them to strengthen our understandings and claims. But dishonest declarations of ‘consensus’ sully the good name and great value of the scientific method.
I do not see the hierarchical social model of peer review as inherently wrong or bad. I am simply aware of chronic corruption by the flows of funding. There are quantifiable crises in science that breed valid skepticism for many existing institutions and processes. It’s harder to tease out bad actors when groupthink infects almost every institution. But suspect behavior is often quite identifiable and can be much more targeted than hastily generalizing to ‘all hierarchical’ scientific review.
“A secondary issue is one of uncertainty: Jessica Hullman and Zeynep Tufekci (among others) have both showed how not communicating the uncertainty inherent in scientific writing has contributed to the erosion of public trust in science [56, 99]. As Tufekci demonstrates (and our data corroborates), the CDC’s initial public messaging that masks were ineffective—followed by a quick public reversal— seriously hindered the organization’s ability to effectively communicate as the pandemic progressed. As we have seen, people are not simply passive consumers of media: anti-mask users in particular were predisposed to digging through the scientific literature and highlighting the uncertainty in academic publications that media organizations elide. When these uncertainties did not surface within public-facing versions of these studies, people began to assume that there was a broader cover-up [98].
But as Hullman shows, there are at least two major reasons why uncertainty hasn’t traditionally been communicated to the public [54]. Researchers often do not believe that people will understand and be able to interpret results that communicate uncertainty (which, as we have shown, is a problematic assumption at best).”PAGE 16
I’ve long shared the view that far more expert debates are appropriate for representative governance in such a complex society. Even if most people aren’t interested, these would help clarify the public understandings closest to academia and independent media outlets. You must also convince the people who are actually questioning and researching the issues.
But instead of hosting debates, the establishment ‘consensus’ just smears ‘skeptical’ experts and literally censors those who share peer-reviewed work on social media platforms. Censorship regarding novel topics of study is anti-science behavior. Claiming ‘consensus’ in support of public health policies without any scientific analysis of the costs of those policies is recklessly irresponsible and has already cost countless lives.
Claiming ‘consensus’ — without disclaiming all significant sources of uncertainty — is anti-science and violates informed consent to this grand social experiment.
“Instead of championing absolute certitude or objectivity, this research pushes us to ask how scientists and visualization researchers alike might express uncertainty in the data so as to recognize its socially and historically situated nature.
In other words, our paper introduces new ways of thinking about “democratizing” data analysis and visualization. Instead of treating increased adoption of data-driven storytelling as an unqualified good, we show that data visualizations are not simply tools that people use to understand the epidemiological events around them. They are a battleground that highlight the contested role of expertise in modern American life.”PAGE 16
Again, they seem somewhat baffled that “orthodox visualizations can be used to promote unorthodox science.” But if us ‘skeptics’ are right, then it makes plenty of sense that we’re able to easily construct so many effect data visualizations. One could apply the same analysis to the ‘consensus’ institutional analysis to understand how and/or why they use ‘orthodox visualizations’ to promote their new scientific paradigm which was inaccurate or not actionable.
I have great respect for anyone who learns and develops complex skills and employs them very explicitly toward the public good or the commons. Even if you’re on “the other side” of a debate, I assume you are also trying to save lives, or at least feed your family. And long ago, I chose to guesstimate that some ~95% of people have great intentions, even if I disagree with their interpretations of the information driving their actions.
But I do also think that groupthink can accomplish an exponential amount of unwitting harm. Gatekeeping ‘the science’ to be curated for a political Overton window must also end, because it already has. Just as they’ve patiently waited to switch to renewables until it’s not a gamble, I’m guessing that the old institutions will eventually participate more with the evolving world of open-source intelligence.
It seems that many people currently support technocracy, consciously or not. Especially this past year, attendees of most science churches have believed many claims without even verifying that those claims are actually published in the publicly available holy texts.
But I do not consent to any further reduction of representation in governance. For decades, I’ve been working to reclaim more representation for we the people. Stronger evidence-based collective decision-making is a great goal. To do so while increasing representation requires catching people up on any relevant scientific topics as they become important. Most humans handle incredibly complex concepts every day. By default, the population must also be treated like rational adults, and we should constantly strive to elevate the national discussion to that of critical thinkers savvy with both data and systems (the work of their government).
If our chronically sick societies want to continue merging medical science with government and politics, then I believe these social experiments would be more ethical on far smaller scales than billions of people. Perhaps next time we run mass experiments with medical interventions, we could find ways to enable communities to ethically opt-in to collect strong data to finally support novel public health policies. Perhaps emergency measures could be voluntarily agreed to before states of emergency are declared. “What do our government prenuptial agreements say about this scenario?”
This concludes my reactions and commentary on the 2021 MIT study. Next, we will discuss my updated conclusions after my rigorous search…
“Akin to, and largely responsible for the sweeping changes in our industrial-military posture, has been the technological revolution during recent decades.
In this revolution, research has become central; it also becomes more formalized, complex, and costly. A steadily increasing share is conducted for, by, or at the direction of, the Federal government.
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The prospect of domination of the nation’s scholars by Federal employment, project allocations, and the power of money is ever present and is gravely to be regarded.
Yet, in holding scientific research and discovery in respect, as we should, we must also be alert to the equal and opposite danger that public policy could itself become the captive of a scientific-technological elite.
It is the task of statesmanship to mold, to balance, and to integrate these and other forces, new and old, within the principles of our democratic system-ever aiming toward the supreme goals of our free society.”TRANSCRIPT OF PRESIDENT DWIGHT D. EISENHOWER’S FAREWELL ADDRESS (1961)
Crisis In Science: Orthodox Consensus Declared Without Compelling Baseline Evidence
Conclusions After a Search for The “Preponderance of Evidence” for Universal Mask Mandate Efficacy
Check the sources, check the sources’ sources, check the sources’ sources’ sources, and you start getting a sense of how much bedrock exists with the strongest evidence.
I know people are passionate about the science they find fascinating — especially if they are consistently scared to believe specific apocalyptic predictions are highly accurate or certain. Given how many fields and systems intersect for such questions, I empathize with the great scientific challenge of trying to ‘prove’ the claims made by passionate mask advocates. But the burden of proof squarely remains on those supporting extraordinary and universal medical interventions.
Out of all the Nonpharmaceutical Interventions (NPIs) widely mandated in 2020, the topics related to masks seem to be among the most studied over the decades. Most community mandate policies cannot be most effectively and ethically studied outside of a state of emergency. If these societal experiments were inevitable, I wish they had been more consensually organized. For example, community-wide medical interventions would have been more ethical if opted into by emergency ballot measures — not governor mandates aided by federal war-time spending.
Over the past year, I probably spent a scattered 15 hours trying to understand the evidence behind the 2020 sub-topic of mask mandates. But in Appendix A below, I spent a fresh 20 hours systematically digging through the three references that the MIT study cited for the alleged “preponderance of evidence that masks are crucial to reducing viral transmission.” This portion of my post is perhaps in the vein of a partial, draft literature review.
Below, I’ve copied various quotes from most studies that I personally found most informative or compelling — for or against universal mask mandates. I have not looked for expert critiques of the most compelling studies, and I’ve only shared little objections or nuances which came to my mind’s epistemological virus scanner. But if anyone does find any of these studies to provide compelling evidence for universal mask mandates, they show also search for expert critiques.
I probably misunderstood some aspects of some of these studies because a some of the fields in these studies are less familiar to me. For example, it’s possible I missed a control arm in some studies if was not clearly referenced in the analysis for comparisons with the experimental mask mandates. Any scholars closer to these fields are welcome to improve on and professionalize this starting point. (There is no need for any attribution or credit to this work, but I would love to read any improvements on my work.)
But I think I now have a relatively strong understanding of how much has been published on this sub-topic. I now have far greater knowledge about and what types of questions have been studied, at least all those cited by the ‘pro-mask’ side of this great debate.
Summary of the “preponderance of evidence” cited to debunk “anti-maskers”:
Four published systematic reviews concluded that there is limited or no compelling evidence supporting the popular assumption that universal mask mandates are likely to reduce viral transmission. (WHO in Oct 2019, CDC in May 2020, The Lancet in Jun 2020, Cochrane Library in Nov 2020)
There were a handful of published multivariate analyses which built very interesting models trying to simultaneously tease out many demographic and novel policy variables. Most are less than a year old and claimed to be among the first to show evidence of mask mandate efficacy. In my humble opinion, these are among the strongest pieces of evidence supporting mask mandates, but they are still very far from proving correlation or causation from mask mandates with any actionable certainty.
A handful of published studies claimed to associate reductions in the rate of change of COVID cases after mask mandates. But only a couple made attempts to show comparisons to any unmasked control arm. And the Kansas study actually showed mask mandate counties to have greater cumulative case numbers overall.
The only strong new randomized controlled trial on masks (not included in previous systematic reviews) appears to be the Danish study with almost 5,000 participants. But they stated that their “findings were inconclusive.”
There were probably 1-2 dozen smaller-scale studies emerging in the past year, most involving less than 1,000 participants each, while studying multiple variables simultaneously. Most used self-reported data, and many did not have a control arm without masks.
Dozens of studies showed compelling evidence that wearing a mask reduces droplets, but likely not aerosols. Only a few of these studies went beyond generalized droplets by testing for potential SARS-CoV-2 transmission and infection reduction.
Dozens of studies detailed the efficacy of different types of masks, different materials, and/or different layering options. Few went beyond generalized droplets to test for viral transmission and infection.
Dozens of expert groupthink opinion pieces selectively citing these scattered and weak new studies. They often acknowledge how little is known, but act as if any one of the new studies had the power to overturn the conclusions of the multiple systematic reviews. (If much of this content had been published by Infowars instead of JAMA, then most would consider them conspiratorial musings. If the sudden face mask ‘consensus’ is more conspiracy than incompetence, then these authors may be unwitting or witting accomplices propagating groupthink.)
The most compelling evidence supporting mask mandates has only just begun emerging over the past six months. Until enough is questioned, replicated, validated, etc, the ‘skeptical’ position here should still be considered very rational and backed up by a lack of established evidence for the ‘consensus’.
Quotes from reviewing the ‘pro-mask’ ‘establishment’ literature:
“Ten RCTs were included in meta-analysis, and there was no evidence that face masks are effective in reducing transmission of laboratory-confirmed influenza.” (WHO systematic review in Oct 2019)
“There is little information on the efficacy of face masks in filtering respiratory viruses and reducing viral release from an individual with respiratory infections …” (Apr 2020)
“Although mechanistic studies support the potential effect of hand hygiene or face masks, evidence from 14 randomized controlled trials of these measures did not support a substantial effect on transmission of laboratory-confirmed influenza. … There is limited evidence for their effectiveness in preventing influenza virus transmission either when worn by the infected person for source control or when worn by uninfected persons to reduce exposure. Our systematic review found no significant effect of face masks on transmission of laboratory-confirmed influenza.” (CDC systematic review in May 2020)
“Previous data from randomised trials are mainly for common respiratory viruses such as seasonal influenza, with a systematic review concluding low certainty of evidence for extrapolating these findings to COVID-19.” (The Lancet systematic review in Jun 2020)
“Systematic reviews and meta-analyses of such studies have provided suggestive, although generally weak, evidence.” (Jun 2020)
“… there are insufficient data on cloth-based coverings, which are being used by a vast majority of the general public …” (Jun 2020)
“There is low certainty evidence from nine trials (3507 participants) that wearing a mask may make little or no difference to the outcome of influenza‐like illness (ILI) compared to not wearing a mask … There is moderate certainty evidence that wearing a mask probably makes little or no difference to the outcome of laboratory‐confirmed influenza compared to not wearing a mask …” (Cochrane systematic review in Nov 2020)
“Several systematic reviews found no conclusive evidence to support widespread use of masks in public settings to protect against respiratory infectious diseases …” (Nov 2020)
“The impact of face masks worn in public on the spread of COVID-19 has yet to be systematically analyzed.” (Dec 2020)
“Prior to the [COVID-19] pandemic, the efficacy of community mask wearing to reduce the spread of respiratory infections was controversial because there were no solid relevant data to support their use.” (Jan 2021)
“Although evidence suggests that masks help to curb the spread of the disease, there is little empirical research at the population level. … Segmented regression analysis of reported mask-wearing showed no statistically significant change in the slope after mandates were introduced; …” (Jan 2021)
If anything, this “preponderance of evidence” confirms that there is very limited evidence “that masks are crucial to reducing viral transmission.” To me, far more progress must be made on topics like this before high certainty becomes actionable for public policy.
The entire framing of the study is within an assumption that anti-maskers are wrong to question this new scientific consensus or orthodoxy. But reading through the citations for that assumption suggests the exact opposite. So I would be curious to know how deeply the MIT team questioned their core premises while designing this experiment.
After reading the appendix below, if anyone still believes that I’ve missed some overwhelming pro-mask evidence here, then I would be curious to understand what it is, and why you find it compelling enough to establish a new public health paradigm.