Also this week: Twitter vs. ducks, Stephen Heard vs. Charles Darwin, a crash course on causal inference, and more.
Former National Museum Wales social media person Sara Huws with a very interesting–and sobering–thread on what makes for viral content about Welsh history. Basically, say that whatever bit of history you’re relating was “suppressed” by some oppressor. Even if it wasn’t. Does this point generalize to social media content about science and academia rather than Welsh history? I have no idea, but I’m sure some of you do; curious to hear your comments. (ht @dsquareddigest)
I’m late to this, but here’s a crash course on Judea Pearl’s ideas about causal inference. Short blog post with a good figure; useful teaching tool. Includes an astute critical discussion of some ecologists’ tendency to just throw a bunch of predictor variables into a GLM and then try to interpret it causally.
It’s the end of
the world theoretical physics as we know it. Because simulations (including physical simulations) are replacing math. Related old post from me musing about a similar trend in ecology. (ht @noahpinion)
Everybody in the world badly underestimates how many people are pretty happy. I link to this mainly because it reminded me that ecologists’ beliefs about many aspects of the ecology faculty job market are incorrect in the pessimistic direction. Now I want to know: in what contexts, if any, are most people’s beliefs incorrect in the optimistic direction? Maybe during economic booms? I’m sure there must be a literature on this (there’s a literature on everything), but I know nothing about it.
This week in dogs biting men: the news topics US adults read about most aren’t the topics they say they want covered. In particular, people say they want more coverage of science and climate change than they actually read. The obvious take here is to compare reading news coverage of science and climate change to eating broccoli or flossing: everybody thinks they should do it but nobody actually does it. The more thoughtful take is to ask how news coverage might have value even if it’s not widely read at the time it’s published. (ht @dsquareddigest)
The future of data science looks like the present of major league baseball. This brief post will be Greek to anyone who hasn’t read books like Moneyball or Big Data Baseball or The Only Rule Is It Has To Work, or websites like FanGraphs. But speaking as someone who has and does, I bet it’s right.
Is the division of research labor between universities (basic research) and corporations (development) good for US economic growth? Or would it be better to somehow bring bad the old Bell Labs model? (ht @noahpinion) (UPDATE: I linked to this mainly because I thought it raised an interesting possibility, but apparently it doesn’t establish its conclusions very well.)
Stephen Heard vs. Charles Darwin on whether one should routinely state the authority for a species’ Latin name.