Archive for the ‘fabio’ Category
Earlier this week, Ann Morning of NYU sociology gave a talk at the Center for Research on Race and Ethnicity in Society. Her talk summarized her work on the meaning of race in varying scientific and educational contexts. In other words, rather than study what people think about other races (attitudes), she studies what people think race is. This is the topic of her book, The Nature of Race.
What she finds is that educated people hold widely varying views of race. Scientists, textbook writers, and college students seem to have completely independent views of what constitutes race. That by itself is a key finding, and raises numerous other questions. Here, I’ll focus on one aspect of the talk. Morning finds that experts do not agree on what race is. And by experts, she means Ph.D. holding faculty in the biological and social sciences that study human variation (biology, sociology, and anthropology). This finding shouldn’t be too surprising given the controversy of the subject.
What is interesting is the epistemic implication. Most educated people, including sociologists, have rather rigid views. Race is *obviously* a social convention, or race is *obviously* a well defined population of people. Morning’s finding suggests a third alternative: race agnosticism. In other words, if experts in human biology, genetics, and cultural studies themselves can’t agree and these disagreements are random (e.g., biologists themselves disagree quite a bit), then maybe other people should just back off and admit they don’t know.
This is not a comfortable position since fights over the nature of human diversity are usually proxies for political fights. Admitting race agnosticism is an admission that you don’t know what you’re talking about. Your entire side in the argument doesn’t know what it’s talking about. However, it should be natural for a committed sociologist. Social groups are messy and ill defined things. Statistical measures of clustering may suggest that the differences among people are clustered and nonrandom, but jumping from that observation to clearly defined groups is very hard in many cases. Even then, it doesn’t yield the racial categories that people use to construct their social worlds based on visual traits, social norms, and learned behaviors. In such a situation, “vulgar” constructionism and essentialism aren’t up to the task. When the world is that complicated and messy, a measure of epistemic humility is in order.
Thomson Reuters has released a press announcement about their predictions for the 2014 Nobel prizes. They project it based on citation patterns. Check out the section on economics:
Mark S. Granovetter
Joan Butler Ford Professor and Chair of Sociology, and Joan Butler Ford Professor in the School of Humanities and Sciences, Stanford University
Stanford, CA USA
For his pioneering research in economic sociology
Kewl!!! Even if Granovetter never wins, he’ll always be recognized as a leader in sociological approaches to markets. And remember, if he does win – WE CALLED IT IN 2007. And yes, we just based it on citation patterns.
H/T: Umut Koc, who posted this on the Facebook orgtheory group.
Over the weekend, I got into an exchange with UMD management student Robert Vesco over the computer science/sociology syllabus I posted last week. The issue, I think, is that he was surprised that the course narrowly focused on topic modelling – extracting meaning from text. Robert thought that maybe there should be a different focus. He proposed an alternative – teaching computer science via simulations. Two reactions:
First, topic modelling may seem esoteric to computer scientists but it lies at the heart of sociology. We have interviews, field notes, media – all kinds of text. And we can move beyond the current methods of having humans slowly code the data, which is often not reliable. Also, text is “real data.” You can easily link what you extract from a topic modelling exercise to traditional statistical analysis.
Second, simulations seem to have a historically limited role in sociology. I find this sad because my first publication was a simulation. I think the reason is that most sociologists work with simple linear models. If you examine nearly all quantitative work, you see that most statistical analyses use OLS and its relatives (logits, event history, Tobit. Heckman, etc). There’s always a linear model in there. Also, in the rare cases where sociologists use mathematical models for theory, they tend to use fairly simple models to express themselves.
Simulation is a form of numerical analysis – an estimate of the solutions of a system of equations that is obtained by random draws from the phase space. You would only need to do this if the models were too complicated to solve analytically, or the solution is too complex to describe in a simple fashion. In other words, if you have a lot of moving parts, it makes sense to do a simulation. Since sociological models tend to be very simple, there is little demand for simulations.
Robert asked about micro-macro transitions. This proves my point. A lot of micro-macro models in sociology tend to be fairly simple and stated verbally. For example, many versions of institutionalism predict diffusion driven by elites. Thus, downward causation is described by a simple model. More complex models are possible, but people seem not to care. Overall, simulation is cool, but it just isn’t in demand. Better to teach computer science with real data.