I am Professor of Sociology at Duke University. → More about me.
Some of my Work
- The Ordinal Society. Harvard University Press. » overview
- Data Visualization. Princeton University Press. » overview
- “Fuck Nuance.” Sociological Theory 35:118-127. » pdf
- “Seeing Like a Market.” Socio-Economic Review, 15:9-29. » pdf
- “The Performativity of Networks.” European Journal of Sociology, 56:175–205. » pdf
- Last Best Gifts. University of Chicago Press. » overview
Recent Writing
Halloween in the Round
Last year I wrote about cleaning some data from the NHTSA FARS database, the system that tracks information about road accidents in the United States. I did that again this year for my Modern Plain Text Computing class. I won’t repeat the cleaning details, which are more or less the same. One question is how to aggregate data like this, if at all, and how to draw a picture of it. There are, as always, lots of options. The data as obtained (with a slightly different query from last year; a small lesson in itself about repeated measures) arrive as counts of pedestrian fatalities (in motor vehicle accidents) for each day of the year, within months, for each year from 2009 to 2023.
Iterating some sample data
I’m teaching my Modern Plain Text Computing course this semester and so I’m on the lookout for small examples that I can use to show some of the ordinary techniques we regularly use when working with tables of data. One of those is just coming up with some example data to illustrate something else, like how to draw a plot or fit a model or what have you. This is partly what the stock datasets that come bundled with packages are for, like the venerable mtcars or the more recent palmerpenguins. Sometimes, though, you end up quickly making up an example yourself. This can be a good way to practice stuff that computers are good at, like doing things repeatedly.
The Road to Selfdom
Marion and I have an essay in Aeon today:
What is happening here is more than an abstract flow of information. It is more than a means of surveillance. It is more than a price mechanism. Rather, it’s as if the air traffic control and insurance commission functions of the IBM 650 have been fused, shrunk, and wholly generalised. This is the real computing revolution. Much of what we do is immediately authenticated as we do it, stored as data, classified or scored on some sort of scale, and deployed in real time to modulate some outcome of interest – usually, the behaviour of a person, or a machine, or an organisation. … Because of this transformation, our sense of who we are is assembled in a strange and tangled fashion. The machinery of ordinalisation attends carefully to individuals rather than coarse classes or groups. By doing so, it appears to liberate people from the constraints of social affiliations and to judge them for their distinctive qualities and contributions. It promises incorporation for the excluded, recognition for the creative, and just rewards for the entrepreneurial. And yet this emancipatory promise is delivered through systems that classify, sort and, above all, rank people with ever-greater precision and on a previously unimaginable scale. The resulting social order is a sort of paradox, characterised by constant tensions between personal freedom and social control, between the subjective elan of inner authenticity and the objective forces of external authentication. It gives rise to a certain way of being, a new kind of self, whose experiences are defined by the push for personal autonomy and the pull of platform dependency.
Blueberry Hill
ChatGPT-maker OpenAI has unveiled the long-awaited latest version of its artificial intelligence (AI) chatbot, GPT-5, saying it can provide PhD-level expertise. Billed as “smarter, faster, and more useful,” OpenAI co-founder and chief executive Sam Altman lauded the company’s new model as ushering in a new era of ChatGPT. “I think having something like GPT-5 would be pretty much unimaginable at any previous time in human history,” he said ahead of Thursday’s launch. GPT-5’s release and claims of its “PhD-level” abilities in areas such as coding and writing come as tech firms continue to compete to have the most advanced AI chatbot.
The Sound of Silence
A GitHub Issue on OpenAI’s Whisper, which is a good speech-recognition and transcription model with support for a large number of languages. A lot of people use it. The issue:
Complete silence is always hallucinated as “ترجمة نانسي قنقر” in Arabic which translates as “Translation by Nancy Qunqar”
In the comments, people note that this class of error has been known for a while and there are equivalents or counterparts in other languages: