some quick fact checks on the election

The election’s over. Trump won. The GOP won the Senate, and kept the House. That’s all the grim truth. No one fully knows how bad this will be for the next four years and for all the years to come, but we have some educated guesses and it’s bad.

Some of the other claims flying right now – about how this happened, who’s responsible, how we missed this, and so on – are misleading or false. This post isn’t a detailed analysis, it’s just a quick attempt to get everyone on the same page. It’s also my meager attempt to deal with the emotional fallout this morning by doing what academics do best: post-hoc analysis and contemplation.

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what’s next?

There is not a lot of silver lining tonight. But here’s one bit: assuming the projections hold, more Americans voted for Clinton than Trump. We live in a messed up system that denies votes to many (Puerto Ricans, disenfranchised felons, etc.), and disproportionately weights the votes of others (the electoral college). So Clinton loses the Presidency despite winning the most votes. But she did win them. The next four years are going to be awful. I am afraid for what will happen to Muslims, to immigrants, to people of color, to women, to anyone targeted by the immense and unaccountable national security state. Elections have consequences, and this one’s consequences rate to be terrible. I shudder especially to think about what this means for tackling climate change – a problem that gets worse every year we fail to act. But at least we can say that most Americans did not vote for this.

So, to that majority of Americans who did not vote for this: What’s next?

asa 2017 call for papers

**Update: The ASA now has a clear link listed on their website. Visit their website for the most up-to-date information**

Because it wasn’t immediately clear where the ASA’s Call for Papers was when I followed the link the ASA is tweeting, I provide it here. Hopefully someone benefits from having it presented in its typical format rather than having to navigate the submission portal to even consider where you might submit something.

social defense systems

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 ouweaponsmath-r4-6-06t 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”:

  1. Is the model opaque?
  2. Is it unfair? Does it damage or destroy lives?
  3. 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.

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latour’s five modes of politics

I just came across a wonderful, short 2007 comment by Bruno Latour titled “Turning around Politics: A Note on Gerard de Vries’ Paper.” Science studies scholars, and Latour in particular, are often accused of having a wonky or non-existent notion of politics. In this essay, Latour goes a long way to clarify what sorts of politics STS scholars have done a good job of exploring, and which sorts they tend to leave by the wayside. Through his reading of de Vries, and his engagements with diverse political traditions from feminism to pragmatism, Habermas to Foucault, Latour identifies five modes of politics, five ways we might mean something is political in a useful sense. All five, he argues, must be part of our study of “cosmopolitics” in Stengers’ useful terminology. Below I reproduce the main chart that summarizes these five modes of politics – of which the first and fifth are often seen as apolitical and which STS and feminist research have made their mission to recast as political (but perhaps, in so doing, lost sight of their distinctiveness from modes 2-4).

Screen Shot 2016-09-30 at 3.11.25 PM.png

I’ll say that I wish I’d read this paper a long time ago, as it provides a much fresher and more synthetic take on “how STS does politics” than Latour’s older (but still wonderful, and totally worth reading), “Give me a laboratory and I will raise the world”, my previous go-to piece for the subject. What do you all think?

the downside of fact checking

The below is an excerpt from my book that seemed relevant to the current moment. It’s presaged by this post from 2012.

Fact-checking during campaigns helps make sure the truth is communicated–but also teaches voters that there is a “right answer” and trains them to listen for true vs. false instead of right vs. wrong.

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a dangerous lack of skepticism

Last weekend, Slate announced the use of social scientific tools similar to those used by campaigns themselves to anticipate results over the course of the day. Slate rejects, in editor-in-chief Julia Turner’s words, the “paternalistic” stance of the traditional media embargo on publishing results during Election Day.

Slate is making a bold move by ignoring the embargo, but in doing so they also appear to be ignoring the flaws of data science and a sacrosanct principle of both social science and journalism: skepticism.

Image by Robert Palmer via Flickr

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