Our long, pandemic inspired video conferencing time has several benefits, including the comfort of having to live and dress the room only from the waist up (in my case, wearing basketball shorts and house shoes within the reach of the webcam), the way which encouraged us to be creative in our approaches to sharing our work.
In March 2021, I was able to give a research seminar at the University of Chicago – to an audience filled with frighteningly intelligent people of high repute – without risking being yelled at or throwing a tomato.
The freedom of videoconferencing encouraged me to try different things. For this seminar, I spent precious time telling the audience about predictions and ideas I was wrong. Not on my broken NCAA bracket, but on the many ways my early assumptions and predictions about the Covid-19 pandemic were incorrect. In doing this, I hoped to challenge myself intellectually (say something smart about my mistake), as well as mask my insecurity, impostor syndrome, and fear of speaking to an audience of extremely intelligent people. This strategy is more than a little pretentious: by dissecting a bad idea in front of everyone, I would signal how really great I was.
The selfish aspects of the approach were not the only motivations for admitting that I was wrong, however. Over the past year, I have been frustrated by the general reluctance of the scientific community to openly discuss when and why we are wrong, and in particular, in our study and prognosis for the pandemic. Our reluctance to point out what we were wrong was a missed opportunity to teach the public about the scientific process, to showcase its necessary ups and downs.
Our aversion to discussing our mistake had dire consequences: we (perhaps unintentionally) overestimated our trust in concepts that were still underdeveloped, alienated many who had legitimate questions, and (ironically) stoked the flames. disinformation and disinformation. For example, the charlatans generated mashup edits of prominent scientists saying one thing about Covid-19 in June 2020, one thing different in August, and something else in November. In response, we mostly offered the same flabbergasted response: “Come on. This is wrong, and that is not how science works. But something is missing from our answers: we could be part of the problem.
What underlies scientists’ inability to deal with mistakes, failures or bad predictions?
It would be easy to pin it on the notoriously large ego of scientists. And while egos fuel many problems in science, I suspect the real reasons for our Covid-19 stubbornness are more complicated.
Since the start of the pandemic, disinformation and disinformation were not mere nuisances, but determining forces in the global response. And their most influential authors were not just renegade “doctors” with YouTube channels, but government officials directly responsible for pandemic policy.
At the very least, bad information has hampered or derailed the public conversation about the science of Covid. The truth is darker: the doubt that was inspired by bad faith actors has led to formal (or non-political) public health policies. Skepticism and denial of science had much higher stakes than the winner of an argument on Twitter. Simple unknowns have been militarized, and many Covid lies have been actively orchestrated and propagated to cast doubt on how science works, sometimes for political ends.
In the face of this, the reluctance of the scientific community to be clear about uncertainties and missteps is not only understandable, but even appropriate: there is a time and place to have abstract debates about the true meaning of “the efficiency ”, and a time to act on the information we have at the service of the public good. The pandemic, and the millions of lives (around the world) we have lost in its wake, are seen as an emergency big enough to forgive a little breathtaking bravado: We’re scientists, we’ve spent decades studying this stuff, and your bullshit is hurting people. We experts and the informed citizen science public can know that science is a process that cannot exist without accumulating new data and rejecting old ideas. But much of the public doesn’t know how this process actually works. Our calls “trust me, I am a scientist” can be misguided.