A few days ago, Mark Suchman, chair of ASA’s OOW section, circulated a Google Doc with a call for people to add movies they use in class to illustrate work and organizational concepts to students. Orgtheory has had a couple of threads on this topic over the years, and I just added a couple of my own favorites (sadly not so current) to the document. I definitely will be checking some of these out next time I teach undergrad orgs — check it out, or add some contributions of your own.
My response to this question on Facebook:
- Do not publish in PLoS if you need a status boost for the job market or promotion.
- Do publish if journal prestige is not a factor. My case: good result but I was in a race against other computer scientists. Simply could not wait for a four year journal process.
- Reviewer quality: It is run mainly by physical and life scientists. Reviews for my paper were similar in quality to what CS people gave me on a similar paper submitted to CS conferences/journals.
- Personally, I was satisfied. Review process fair, fast publication, high citation count. Would not try to get promoted on the paper by itself, though.
- A lot of people at strong programs have PLoS One pubs but usually as part of a larger portfolio of work.
- A typical good paper in PLoS is from a strong line of work but the paper just bounced around or too idiosyncratic.
- PLoS One publishes some garbage.
- Summary: right tool for the right job. Use wisely.
Another person noted that many elite scientists use the “Science, Nature, or PLoS One model.” In other words, you want high impact or just get it out there. No sense wasting years of time with lesser journals.
The Society for the Advancement of Socio-Economics (SASE) website has made Neil Fligstein‘s powerpoint slides on the history of economic sociology available for general viewing. It’s a fascinating read of the development of a sub-field across continents, and it also includes discussion of a challenge that some believes plagues the sociology discipline:
Both Max Weber and Thomas Kuhn recognized that Sociology as a discipline might be doomed to never cumulate knowledge.
- Sociology would proceed as a set of research projects which reflected the current concerns and interests of a small set of scholars
- When the group hit a dead end in producing novel results, the research program would die out only to be replaced by another one
- Progress in economic sociology is likely to be made by putting our research programs into dialogue with one another to make sense of how the various mechanisms that structure markets interact
- Failure to do so risks the field fragmenting of the field into ever smaller pieces and remaining subject to fashion and fad
Fligstein’s claim for these field-fragmenting tendencies stems from the current structure of the academic field. He depicts sociology as rewarding scholars for applying ideas from one area to another area where current theorizing is insufficient, rather than expanding existing research:
- … the idea is not to work on the edge of some mature existing research program with the goal of expanding it
- But instead, one should be on the lookout for new ideas from different research programs to borrow to make sense for what should be done next
In short, scholars tend to form intellectual islands where they can commune with other like-minded scholars. Bridging paths to other islands can generate rewards, but the efforts needed to disseminate knowledge more widely – even within a discipline – can exceed any one person’s capacity.
Roger E. Farmer has a blog post on why economists should not use complexity theory. At first, I though he was going to argue that complexity models have been dis-proven or they use unreasonable assumptions. Instead, he simply says we don’t have enough data:
The obvious question that Buzz asked was: are economic systems like this? The answer is: we have no way of knowing given current data limitations. Physicists can generate potentially infinite amounts of data by experiment. Macroeconomists have a few hundred data points at most. In finance we have daily data and potentially very large data sets, but the evidence there is disappointing. It’s been a while since I looked at that literature, but as I recall, there is no evidence of low dimensional chaos in financial data.
Where does that leave non-linear theory and chaos theory in economics? Is the economic world chaotic? Perhaps. But there is currently not enough data to tell a low dimensional chaotic system apart from a linear model hit by random shocks. Until we have better data, Occam’s razor argues for the linear stochastic model.
If someone can write down a three equation model that describes economic data as well as the Lorentz equations describe physical systems: I’m all on board. But in the absence of experimental data, lots and lots of experimental data, how would we know if the theory was correct?
On one level, this is a fair point. Macro-economics is notorious for having sparse data. We can’t re-run the US economy under different conditions a million times. We have quarterly unemployment rates and that’s it. On another level, this is an extremely lame criticism. One thing that we’ve learned is that we have access to all kinds of data. For example, could we have m-turker participate in an online market a million times? Or, could we mine eBay sales data? In other words, Farmer’s post doesn’t undermine the case for complexity. Rather, it suggests that we might search harder and build bigger tools. And, in the end, isn’t that how science progresses?
Over at Race, Politics, and Justice, Pamela Oliver asks why her home state of Wisconsin has such high rates of Black imprisonment in comparison to other states, even in times when rates are falling:
Wisconsin has stayed at the top of the pile in Black incarceration even though its Black incarceration rate has been declining. How can this be? The answer is that all the other states have been declining faster. By putting a scatter plot of state imprisonment rates on consistent axes, I’ve been able to produce a really cool animation effect. The data source is the\ National Corrections Reporting Program public “in custody” file. Rates are calculation on entire population (all ages). States voluntarily participate in this data collection program and appear and disappear from the plot depending on whether they reported for the appropriate year. States are also eliminated if more than 10% of their inmates are recorded as having unknown race. You’ll see if watch long enough that the relative positions of most states stay the same, but the whole distribution starts moving downward (lower Black incarceration rates) and to the left (lower White incarceration rates) in the last few years. You may download both these images and explanatory material in PDF format using this link.
Interesting. This is a classic example of the “dog that didn’t bark.” What happened in other states that did not happen in Wisconsin? A few hypotheses: Wisconsin reflects particularly bad conditions in segregated places like Milwaukee; fixed effects of prosecutors – Wisconsin district attorney’s are notoriously bad; police enforcement is unusually harsh. Add your hypotheses or explanation in the comments.
Dear friends and readers,
This coming Winter, Columbia University Press will publish my next book, Theory for the Working Sociologist. The book is my attempt to present social theory in a way that is accessible to upper division social science students, graduate students, and any reader who just wants to know what sociology is up to these days.
The book has an intuitive organization. I choose four major themes of social theory and explain the general ideas (“theory”) that motivate concrete empirical studies and explanations (“mechanisms”). For example, the first section of the book is about power and inequality theory. I illustrate how theoretical ideas about habitus and intersectionality are represented in empirical research and how they grow from earlier approaches to power and inequality. I also have sections on social construction, values/structures/institutions, and strategic action theory (i.e., social capital, structural holes, rational choice and other ways sociologists talk about purposeful action).
The book is short and designed to be used in many contexts. In my undergraduate theory course, I used the draft of the book to supplement original texts. After reading various inequality theorists from Marx to Patricia Hill-Collins, I assigned chapter 2 to provide an overview of how inequality theory has developed.
Due to its short length, it is also well suited for a quarter course on contemporary theory or as the text you read after you plow through the classics. I can also imagine that graduate students might enjoy it because it offers a brief survey of the major theories of sociology. Many sociologists start in related fields, like political science or economics, and need a “tour guide” to help them find their place.
Finally, I want to thank the readership of this blog. I acknowledgments list many readers who read the text and improved it and the readers who encouraged me to write it in the first place.
If you are thinking of assigning this book in your course, please email me and I will send you the (almost) final draft.