The COVID-19 pandemic is threatening the lives of people worldwide. It has certainly increased the level of risk we are all exposed to. Ulrich Beck and others have described our times in terms of a risk society, and the pandemics seem to confirm this view. Of course, exposure to risk has always been unequal. The marginalized sectors of the world economy have always been exposed to high levels of risk (from illnesses, to violence, to exclusion), while the economic elites and the middle classes have been able to protect themselves and to some extent shelter themselves from risk. And exposure to the coronavirus, access to health care, and the risk of losing one’s life are highly correlated to class and to race in our racialized class system. But the pandemic has increased the risk for all, mounting the risks of people who already live precarious lives, but also putting at higher risk the lives and livelihoods of people who usually enjoy some mechanisms of protection from the “conventional” risks we were used to living with. It is hard to think of a previous crisis that has affected all parts of the world at the same time and in such a radical way (at least within the memory of most living people).
A specter is haunting the U.S. education system—the specter of not being able to carry out the routine administration of standardized tests. While written achievement tests were considered controversial in U.S. schools throughout the 19th century, by the mid-20th century it became acceptable to measure the “merit” of individuals via instruments such as IQ tests. Nowhere was this more true than in the higher education system, where competition between institutions led to shifting definitions of merit and to assertions about the role standardized testing should play in a meritocracy. Today, in the middle of a public health crisis that makes such testing difficult (if not impossible), both critics and advocates of standardized testing are raising new questions about teaching and the measurement of learning in the U.S. What role will academics—and teachers, students, university administrators, and others—play in this process?
The trolley problem is a classic thought experiment in moral philosophy. A quick version is: should you pull a switch to change the tracks of a moving trolley to hit only one person when it’s currently on a set of tracks that will lead it to hit five?
A recurrent feature of game-theoretical economics, political science and sociology is the principal-agent problem. Many phenomena in the social world can be described in terms of the (various) theories of principals and agents. Want to understand how Southwest Airlines broke into the industry? Why presidents do not exit losing wars? Why it was an advantage for Kennedy in his standoff with Khrushchev to have a “rogue” general who favored nuclear war? Why corruption is not only a collective action problem? Principal-agent theory is here to help! A principal sends an agent to do a task, under some kind of contract or agreement, establishing a relationship subject to certain constraints, and open to certain possibilities. If we can describe these constraints and possibilities, we can explain a lot. Does agent know more than principal? Does principal have the capacity to punish agent, reward agent, or both? And so on.
It’s hard to even begin blogging about something so vast and ever-shifting. This post is just going to be a short pointer to a couple of the best pieces I’ve seen covering the social and economic angles of the pandemic.
The following is a guest post by Juan Pablo Pardo-Guerra.
Topic models are fast emerging as a workhorse of computational social science. Since their introduction in the late 1990s as part of a larger family of classification and indexing algorithms, they have grown into one of the most common and convenient means for automated text analysis. Not too long ago, using topic methods confronted scholars unfamiliar with programming with steep learning curves: even the simplest implementations required some familiarity with coding in addition to a good deal of patience. Today, by contrast, topic modeling is available as part of point-and-click desktop applications (e.g. Context) and can be installed in widely used statistical analysis packages (e.g. Stata). The relative ease, scalability, and intelligibility of topic models explains, perhaps, their quick adoption across sociology, political science, and the digital humanities. Indeed, to say that topic models are the OLS of text analysis wouldn’t be too much of an exaggeration.
Earlier this week, I was asked to help organize an event for graduate students seeking advice on the “responsible use of twitter for grad students.” Of course, my first instinct was to crowdsource advice from #SocTwitter itself. In this post, I gather together some of the advice suggested by others, including a list of already published or posted resources and guides.