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Interested in applying to Monica Prasad’s Problem-Solving Sociology Workshop?  Here’s the information:

Call for Applications: Problem-Solving Sociology Dissertation Proposal Development Workshops

Doctoral students in departments of sociology who have not yet defended their dissertation proposals are invited to apply to dissertation proposal development workshops on “problem solving sociology.”  Northwestern University will pay for economy-class airfare and accommodation in Evanston, IL, plus meals and transportation expenses, for a one-day preliminary workshop as well as a one-day final workshop.  These workshops are made possible by a grant from the National Science Foundation.

Problem-solving sociology seeks to use sociological theory to shed light on solving (not just describing) contemporary social problems, and seeks to use investigation of these problems to further sociological theory.  The approach proceeds from the assumption that mitigating critical social problems can be a catalyst for breakthroughs in the basic understanding of society.

Workshop participants will attend two one-day workshop events: a preliminary workshop (November 29, 2018, or December 6, 2018) to introduce the approach and give preliminary feedback to students’ ideas, and a final workshop (May 23, 2019 or May 30, 2019) to give more detailed feedback on students’ full dissertation proposals.

To apply, please submit by September 30, 2018, to mirieliyahu2014@u.northwestern.edu a short cover letter detailing your university, your year in the program, whether or not you have defended your dissertation proposal and what date you expect to defend it, and any other information that might be relevant (including if one of the dates above does not work for you—but please note that in that case we may not be able to accommodate you at all); and a separate document, no more than 2 single spaced pages, responding to some or all of the following questions (not all questions will be relevant for all applicants):

1)     What is the social problem that you seek to solve?  What are some potential solutions, and how can research shed light on how to move forward with solutions?

2)     What social theories or approaches might be useful in solving this problem?  If none, can you use this research as a way to critique and reformulate existing theories?

3)     (more relevant for some topics than others) Have you been involved with non-academic groups that work on this problem?  Describe if so, or if you have plans to be in future.  Do you see a way to engage sociological theory with the work of these groups?

4)     (if possible) How could short-term solutions feed into longer-term, structural change on this problem?

We welcome both creative and ambitious ideas, as well as focused and practical ideas, as well as ideas that are somewhere in between.  If the problem is the basic structure of the economic system and the only solution that you see is revolution, then think about how to bring about revolution.  If the problem is colleges closing over spring break and low-income students having nowhere to go, think about how to get institutions to respond to the needs of nontraditional members.  If the problem is racism or sexism, think about how to solve (not just describe) racism or sexism.  If you already know the solution to the problem, but the problem is convincing policymakers, then focus on how to convince (or change) policymakers.

Problem-solving sociology is discussed in the latest issue of Contemporary Sociology but we are less interested in whether or not you have read this material and more interested in hearing your original ideas.

This is Gabriel’s rant about R from the first episode.

I’m not going to talk about things that are necessary features of the language. I get that the flexibility of an object-oriented language that can handle relational data requires a certain learning curve. But it doesn’t have to be as painful as R. I may be a Stata user so I come with the expectation that there’s just a single flat-file database in memory, but I also know Python so I know damn well an object-oriented language doesn’t have to be this infuriating.

  • A tutorial culture that doesn’t represent a realistic workflow. Every R book or MOOC I have ever read or taken spends the first few hours on stuff like vectors, matrices, and frames before showing you basic workflow things like loading a file from disk and getting summary statistics for the variables. If R’s tutorial culture was Rick James it would be stomping its muddy boots on your couch shouting “fuck your learning curve”
  • A documentation culture that doesn’t represent a realistic workflow. The examples in manual entries are more likely to start with a bunch of colons and commas than read.table().
  • A documentation culture that doesn’t show output, just a script. If I could get it to work, I wouldn’t be reading the documentation. Knowing what I should be looking for would be really helpful.
  • Output, we don’t need no output. We don’t have to show you any stinking output.
  • The default arguments are ridiculous. Every time I loop over a bunch of filepaths or URLs, I cry to the heavens in anguish that paste() defaults to interpolating a bunch of spaces unless you explicitly tell it not to.
  • camelCase, vs _, vs .
  • The default functions are awful and everyone knows it, which is why there’s a library that replaces every function in base R that runs faster and with a simpler syntax and people are shocked that you would run a “for” loop like in any other language instead of vectorizing.
  • RStudio encourages you to save session memory, which in turn encourages really bad habits for reproducibility.
  • CRAN is such a pain to deal with that library developers post their code to GitHub, which is a pain for users to install, especially if there are dependencies. Keeping your R libraries working is like using Linux in 1995. I got so annoyed with getting the update on an R library to work that I found it easier to just learn Python.
  • Even when you call output, the default output is ridiculous. When I look at a model fit object I wonder, where the fuck are the standard errors? I know this is why you do the summary of the lm object, not the lm object itself, but that it’s an extra step to get meaningful output is another example of bad default assumptions that yields gratuitous hostility to the user.
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