Information about COVID-19 right now is piecemeal, sometimes contradictory and always at least somewhat uncertain. The scruffiness resembles how research might look as it assembles its models of reality, otherwise known as knowledge.
But research is a structured conversation through the literature, a call and response that confirms advances and signals dead ends in its reality models. By contrast, the COVID-19 information environment for the public is the media ecosystem — unstructured and easily hijacked.
We see this contrast clearly in the receptivity (both in information producers and consumers) for certainty about COVID-19 and confidence in what’s being said. The monumental uncertainty about the virus (and the almost intolerable anxiety that uncertainty provokes) means that anyone who can make declarations with confidence will code as an authority — and that existing authorities, in turn, feel compelled to overconfidence. That’s why US Vice-President Pence is assuring Americans that domestic air travel is still safe. That’s why normally careful, research-savvy journalists such as New York Times columnist Farhad Manjoo and Vox’s Kelsey Piper initially declared that COVID-19 wouldn’t be a big deal, before later being forced by events to walk back those claims. Accountability systems in politics, in opinion journalism and on social media are weak forces. The premium is on looking in command.
Research’s accountability mechanisms — peer review, open data, impact measures, replication and incrementalism — are designed in part to guard against premature confidence, although the incentives for big-splash papers are pushing hard against these guardrails. There are, at least theoretically, always more questions to be answered.
The problem comes when researchers lead with uncertainty in public, and let it muffle and smother whatever they came to assert. The norms of research culture — what gives you recognition, credibility and authority — are what take away recognition, credibility and authority in the public square. So how do we make assertions in public that aren’t just empty certainty, that distinguish us as researchers and acknowledge our uncertainty without being choked by it?
Max Fisher, a New York Times reporter who co-writes that newspaper’s “The Interpreter” column with his colleague Amanda Taub, provides one model in his recent piece “How Worried Should You Be About the Coronavirus?” in which he immediately acknowledges “it’s a complicated question for two reasons”:
- First, we still don’t know a lot about the disease, including how widely it has spread, just how contagious it is or how deadly it is;
- Second, risk is not a question just of your individual vulnerability to the disease, but the the vulnerability of the health and economic systems in your society as well as your ability to socially distance yourself (e.g., can you work at home or do you have to continue going to a workplace?).
The piece is full of the words “maybe” and “if” — full of contingencies and unknowns. And yet, it is the clearest article I’ve read summarizing the state of knowledge on risk, because it proceeds from a clear framework of analysis that you can use to start to evaluate your own situation.
Farhad Manjoo’s follow-up column admitting he was initially wrong about coronavirus swung the other way, exemplified by its headline “Admit It: You Don’t Know What Will Happen Next.” But cynicism about knowledge (and renouncement of confidence) is as facile as overconfidence (and just as typical for reporters).
True confidence (our confidence in what you are saying) comes from your acknowledging uncertainties but leading with your framework of analysis and your evidence. Uncertainty thus becomes not a liability but an essential pillar in confidence building — as long as it doesn’t overwhelm what you’re arguing and the framework from which it flows. Framework + evidence + acknowledgement of major uncertainties and what is yet to be known: the formula that sets researcher authority apart.