Note – this is an experimental posting format. I’ve thought about increasing the number of posts I commit to per week, but I don’t want to add unnecessary work if I’m not willing to stick it out. Let’s be honest: sometimes it’s really hard to get a single post out each Monday that I’m satisfied with, so increasing my posting frequency just to for the sake of increasing my output is a terrible idea. I will run a short experiment to see how easy it is for me to get out a Friday Round-up for the next month. If the experiment goes well, I’ll consider making it a part of the regular rotation. You can find the first round-up post here from April 24th.
Have you ever noticed the tendency that when you’re thinking about a topic, you seem to notice it everywhere? I first became aware of its phenomenology back in my university days, where stuff that I was learning in my lectures seemingly popped up randomly in my non-class time. Turns out, there is a word for that feeling – the Baader-Meinhof phenomenon, also known as the frequency bias. It’s why you start to see your car’s model everywhere after buying one. I bring this up because today’s articles are all loosely connected with scientific literacy in the digital age (especially as it relates to COVID). The more I read about thoughts concerning how to understand research about the pandemic, the more content I noticed about the topic of scientific literacy in general. This might be the phenomenon/my bias at play, or maybe the algorithms that govern my feeds are really in tune with my concerns.
Here is my round-up list for the week ending on May 1st:
📖Article – What You Need to Know about Interpreting COVID-19 Research | The Toronto Star
My round-up for the week started with this short article that was open in one of my browser tabs since last week. There is a lot of information floating around in our respective feeds, and most of it can charitably be called inconclusive (and some of it is just bad or false). We’ve suddenly all become “experts” in epidemiology over the last month, and I want to remind myself that just because I think I’m smart, doesn’t mean I have the context or experience to understand what I’m reading. So, this article kicked off some light reflection on scientific and data literacy in our media landscape.
📖Article – Experts Demolish Studies Suggesting COVID-19 is No Worse Than Flu | Ars Technica
This next piece pairs nicely with our first link, and includes reporting and discussion of recent flair ups on Twitter criticizing recent studies. Absent of the pressure being applied by the pandemic, what this article describes is something that normally takes place within academic circles – experts putting out positions that are critiqued by their peers (sometimes respectfully, sometimes rudely). Because of the toll the pandemic is exacting on us, these disagreements are likely more heated as a result, which are taken to be more personally driven. I link this article not to cast doubt over the validity of the scientific and medical communities. Rather, I am linking to this article to highlight that our experts are having difficulty grappling with this issues, so it’s foolish to think us lay-people will fare any better in understanding the situation. Therefore, it’s incumbent on journalists to be extra-vigilant in how they report data, and to question the data they encounter.
📽Video – Claire Wardle: Why Do We Fall for Misinformation | NPR/TED
The Ars Technica piece raises a lot of complex things that we should be mindful of. There are questions such as:
- Who should we count as authoritative sources of information?
- How do we determine what an authoritative source of information should be?
- What role does a platform like Twitter play in disseminating research beyond the scientific community?
- How much legitimacy should we place on Twitter conversations vs. gated communities and publication arbiters?
- How do we detangle policy decisions, economics, political motives, and egos?
- How much editorial enforcement should we expect or demand from our news sources?
There are lots of really smart people who think about these things, and I’m lucky to study at their feet via social media and the internet. But even if we settle on answers to some of the above questions, we also have to engage with a fundamental truth about our human condition – we are really bad at sorting good information from bad when dealing at scale. Thankfully, there are people like Claire Wardle, and her organization FirstDraft that are working on this problem, because if we can’t fix the signal to noise ratio, having smart people fixing important problem won’t amount to much if we either don’t know about it, or can’t action on their findings. I was put onto Claire Wardle’s work through an email newsletter from the Centre for Humane Technology this week, where they highlighted a recent podcast episode with her (I haven’t had time to listen to it as of writing, but I have it queued up: Episode 14: Stranger Than Fiction).
📖 Essay – On Bullshit | Harry Frankfurt
All of this discussion about knowledge and our sources of it brought me back to grad school and a course I took on the philosophy of Harry Frankfurt, specifically his 1986 essay On Bullshit. Frankfurt, seemingly prescient of our times, distinguishes between liars and bullshitters. A liar knows a truth and seeks to hide the truth from the person they are trying to persuade. Bullshit as a speech act, on the other hand, only seeks to persuade, irrespective of truth. If you don’t want to read the essay linked above, here is the Wikipedia page.
I hope you find something of value in this week’s round-up and that you are keeping safe.
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