Archive for the ‘sociology’ Category
I got sick of navigating the ASA Meeting Calendar thing, so I threw together something some of you might find useful. You can see what’s happening on various days, but also—and this is the potentially useful part—every event has an associated
.ics file for you to download and import into your preferred calendar application such as iCal, Outlook, Google Calendar, and so on. Dates, times, summary information, and locations included. Enjoy.
This August 15, Alex Hanna, a computational sociologist at Wisconsin, will host “Big Cities, Big Data” at the campus of UC Berekeley. BC/BD is a “hackathon” – a meeting of people who program all night long to develop new projects. The next day, the results will be presented at a workshop at ASA. From the announcement:
The theme is “big cities, big data: big opportunity for computational social science,” the idea being looking at contemporary urban issues — especially housing challenges — using data gathered and made publicly available by cities including San Francisco, New York, Chicago, Austin, Boston, Somerville, Seattle, etc.
The hacking will start at noon on August 15 and go until the next day. Sleeping is optional. We’ll have a presentation and judging session in the evening of August 16 in San Francisco, exact location TBD.
We’re working with several academic and industry partners to bring together tools and datasets which social scientists can use at the event. So stay tuned as that develops.
Check it out! It’s the place to meet the next generation of sociology hackers!
A few days ago, I suggested that sociologists should seriously consider teaming up with computer scientists. Here, I’d like to sketch out the big picture to suggest why we are in a special moment. Basically, computer science has had three major stages of development:
- Stage 1 (1949-1970s): The construction of computers. In this stage, it was all about the engineering. How could you make a machine that (a) could be programmed, as opposed to running one command, and (b) do it in a way that didn’t require a machine the size of a house?
- Stage 2 (1970s-1990s): Learning and theory. Could you make a machine that could, say, solve an algebra equation? Play chess? See things? CS also developed its mathematical side. Does this algorithm find an answer in a reasonable amount of time?
- Stage 3: (1990s-present): Social computers. Can we build machines that will help people, say, trade using e-currency? Operate in secure networks? In other words, instead of making computers mimic people, we make computers extensions of people.
Of course, people still work in all streams of computer science. The issue is that the social computing stream is now huge. That means that computer scientists are building a technical system that integrates human beings and computer networks. In other words, there isn’t going to be real sharp distinction between online behavior and “real world” behavior. They’ll be connected.
A second observation is that social computing is the engineering analog of “social action.” It’s a broad idea that encompasses a lot of behavior. This is a bit different than say, economics, which reduces a lot to price theory, or political science, which focuses on very specific things like voting or legislation. Instead, computer scientists are dealing with something that is extremely broad. That’s why they can entertain all the different types of data: video recording how people use computers, text analysis, online experiments, and plain old vanilla stats.
None of this means that the CS/soc hookup will automatically happen. Rather, this post explains why this opportunity has appeared. It’s up to us to make the most of it. Otherwise, you can bet on a series of Nature and Science articles that are sociological, but lack sociology authors.
Nicolai “The Postmodernist” Foss recently drew my attention to the blog of sociologist Randall Collins. I had never read it before, but I’ve been missing out. My guess is that it documents Collins’ recent thoughts on topics that he’s working on. Examples:
Infrequent, but always good. Recommended.
This week, some readings on who goes to college and why:
- College Choice in America by Charles Manski. The standard model of how students choose the college they attend. Just add $40k to the tuition bill to update it.
- Read a bunch of reports summarizing the results of the annual freshman survey fielded by UCLA’s Higher Education Research Institute. Start with the 70s and move forward.
- How elite schools choose students: Crafting a Class by Duffy and Goldberg; Creating a Class by Mitchell Stevens; and The Chosen by Jerome Karabel. Each a classic in its own way.
- Academically Adrift by Arum and Roksa. Shows the limited learning in higher ed.
- Read Becker on human capital, Arrow & Spence on signalling, and Collins on credentialism. Each is a classic statement of different theories of how college plays into employment and income.
- Read Carnevale, Strohl and Melton on the incomes associated with different college majors.
- On student protest: Freedom’s Web by Richard Rhoads and From Black Power by myself.
Use the comments for more suggestions.
sociology compass article by Liz Gorman now available: “Professional Self-regulation in North America: The Cases of Law and Accounting”
Professional and expert work holds the potential for misconduct that can harm clients or the public. According to the traditional model of professional self-regulation, developed during the “golden age” of the professions in the mid-20th century, societies grant professional communities freedom from external regulation in return for their commitment to regulate their members’ conduct. Professions were said to cultivate distinctive ethical norms, socialize new practitioners, and engage in social control of deviant behavior. In light of dramatic changes in the professional world since that time, this essay reviews research on the legal and accounting professions in North America to assess the extent to which this traditional model still holds. The two professions continue to resemble the traditional model in some respects but diverge from it in others, and on some points, there is insufficient evidence to draw a conclusion. The traditional model of self-regulation is probably best viewed as an ideal type that can serve as a standard of reference, not as an accurate representation of social reality. This conclusion opens up new topics for research and opportunities to inform policy.
Yesterday, I described how Indiana sociology conducts graduate education. It’s a really good system. In fact, when prospective students visit, I describe the system and then say: “Look, if you get an offer from Princeton, take it! They have prestige, money, and an amazing placement record. But if you don’t have an offer from a place like that, you are probably making a mistake if you turn us down.”
Still, nothing is perfect and it is worth talking about the drawbacks of the Indiana model. First, the system only works because most faculty do research where it is easy to get people involved. We do a lot of survey research, interviews, health, education, and public opinion. Thus, it’s probably not the best place for doing certain types of research that are not large team based like ethnography or comparative historical. Our students do well in those areas, but there are other better options out there.
Second, we don’t do well with what I call the “Foucault” kids. These are students who have some insanely interesting project that spans disciplines and is very sui generis. These students need less structure, not more of it. They don’t need all the stuff that IU provides. All they need is one or two older scholars who can give some honest feedback and make sure they don’t take 12 years to finish. I encountered one such student during a visit. This student unleashed this insane ethnographic/multi-site study of health on me and I said: “You are clearly very good. Just go to Harvard. Tell Bob I said hello.
Mind you, a lot of people think they are Foucault, but they’re not. IU is built for people who want to do high quality work within the confines of normal social science – which means most of you! For a few people, or people in specialties where the model doesn’t fit, it may not be appropriate.