Some background: since 2009 I’ve been working on grade transparency as one policy response to grade inflation, grade compression, and grade inequality at UNC. (See here, here, here, and here, among others.) After many, many meetings, conversations, presentations, and discussions, at last week’s Faculty Council meeting the Educational Policy Committee delivered a report on the policy that may well signal its demise. Below are the comments I made at the Faculty Council meeting in the discussion on that report. Continue reading “on carolina contextual transcript policy’s woes”
The seductive power of sensual charm survives only where the forces of denial are strongest. If asceticism once reacted against the sensuous aesthetic, asceticism has today become the sign of advanced art. All “light” and pleasant art has become illusory and false. What makes its appearance esthetically in the pleasure categories can no longer give pleasure. The musical consciousness of the masses today is “displeasure in pleasure” — the unconscious recognition of “false happiness.”
–Adorno, “On the Fetish-Character in Music and the Regression of Listening,” 1938
Jeff Guhin innocently posted to Facebook that “doing a lecture on Habermas is ridiculous.” He may well be right, for many different kinds of reasons. But in the (lengthy!) conversation that followed, two critiques were raised that I think deserve separate treatment. They are:
- That much theory, including Habermas and, all the more so, his Frankfurt predecessors, is too difficult to read to make it worthwhile; and
- Reading theorists like Habermas is really mostly about the history of social thought and has no payoff for empirical or analytical sociology.
I think both of these are wrong.
I started teaching Cathy O’Neil’s book Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy in my class last week. Despite being a mathematician by training (she goes by the moniker mathbabe online), the book makes a strong case for the importance of social science generally and sociology in particular.
O’Neil looks out at the land of big data and its various uses in algorithms and sees problems everywhere. Quantitative and statistical principles are badly abused in the service of “finding value” in systems, whether this be through firing bad teachers, targeting predatory loans, reducing the risk of employee turnover by using models that incorporate past mental health issues, or designing better ads to sniff out for-profit university matriculates. Wherever we look, she shows, we can find mathematical models used to eke out gains for their creators. Those gains destroy the lives of those affected by algorithms that they sometimes don’t even know exist.
Unlike treatises that declare algorithms universally bad or always good, O’Neil asks three questions to determine whether we should classify a model as a “weapon of math destruction”:
- Is the model opaque?
- Is it unfair? Does it damage or destroy lives?
- Can it scale?
These questions actually eliminate the math entirely. By doing so, O’Neil makes it possible to study WMDs by their characteristics not their content. One need not know anything about the internal workings of the model at all to attempt to answer these three empirical questions. More than any other contribution that O’Neil makes, defining the opacity-damage-scalability schema to identify WMDs as social facts makes the book valuable.
I hope that you will forgive the shameless self-promotion, but I recently published a paper in Sociological Science (yay open access!) that examined neighborhood racial change in New York, Los Angeles, Chicago, and Houston metropolitan neighborhoods with an amazingly talented colleague, Siri Warkentien.
We find mixed results related to future racial integration. On the negative side we find that recent estimates overestimate the stability of long-term racial integration. Previous studies don’t really examine the pace of neighborhood change, which reveals many integrated neighborhoods are in fact resegregating.
On a more positive note, we find that some neighborhoods really do maintain multiethnic segregation over many decades. We call those neighborhoods “quadrivial neighborhoods,” which, in Latin, means four roads coming together. These neighborhoods emerged during the 1990s and seem to make up the fastest-growing category of neighborhood in the past couple of decades (though they are not coming about as fast, nor are they as common as some have estimated).
One of the contributions that I hope we make is showing the geography of neighborhood change. Unlike previous studies, we map where different types of neighborhood changes occur. The model assigns the probability of membership to different types of neighborhood change for each neighborhood (which we defined as Census tracts); we then mapped the results. You can look for yourself on the website which we built for the project. These might be helpful if you are teaching about neighborhood change or segregation, particularly in one of the four metro areas that we studied.
The big take-aways? The black “ghetto” — that area created by malign and benign neglect of black neighborhoods — has expanded out into the outlying suburban communities (places outside of New York and Chicago that are akin to Ferguson in St. Louis.). Increasing Latino and Asian segregation looks more like a checkerboard. Pockets of increasing Latino population are surrounded by neighborhoods experiencing less or slower racial change. And finally, those quadrivial neighborhoods? Not in central cities where we focus on the diversity of the creative class. Almost all are in the suburbs or, at the very least, outlying neighborhoods in the city.
The moral, as far as I can tell from out study: racial segregation will continue to be a problem; and if you want to live somewhere really racially diverse, start looking in the ‘burbs.
In the Washington Post earlier this week, Steve Pearlstein published a piece promoting four things universities should do to cut costs:
- Cap administrative costs
- Operate year round, five days a week
- More teaching, less (mediocre) research
- Cheaper, better general education
The next day, Daniel Drezner responded with four things columnists should do before writing about universities.
- Define what you mean by “universities.”
- Don’t exaggerate the problems that actually exist.
- Don’t rely on outdated data.
- Be honest that you’re using higher ed reform as an implicit industrial policy.
intro theory class yesterday I did an exercise using PollEverywhere to evoke associations between musical taste and identity. I played four musical pieces and asked the students to type free-text responses to “What kind of people like this song?”. Their responses were lots of fun, and I present them below in raw form for your enjoyment, interest, and comment.
I’m against active learning. Well, maybe not against it. Would you settle for “less for it than others are?” Here’s why.