I really didn’t want to write about the election. But probably, much like you, it’s all I can think about right now. News media is completely saturated with it and the blogs are churning out a steady stream of predictions and post mortem.
Even among those with college degrees and 2016 election was a divisive one.
When we consider voters with post-graduate degrees, then we really start to see the ivory tower effect.
As an academic, I’m really troubled, but also fascinated by what this election and the reactions to it on college campuses tells us about the state of higher ed. Many of us wrestled with what to do last Wednesday when we stood in front of a room full of wide-eyed millennials. As Beth wrote over on the blog PhD Plus Epsilon, it was tough. The responses on college campuses have been extreme, and they tell a story perhaps different from the one we imagined.
On the one hand, the popular notion is of the ivory tower as a liberal bastion, and yet news anecdotes are giving the impression of college campuses which are massively divided, even in the least purple of states. But after the dust begins to settle, we can begin to try and understand more by looking at the numbers.
Several data sets published by the researches at The Chronicle analyze the voting outcomes over the past several elections across different swaths of the academic ecosystem. And it appears that our students (being people, I guess technically, without college degrees) have a much greater polarity that we do on the faculty. From these numbers, the whole liberal bastion ivory tower business doesn’t even seem to apply to students in the universities. For reasons I won’t delve into right here and now, I consider this a bit strange.
This data, gathered by the Chronicle of Higher Ed, shows a tendency towards the left in counties housing flagship universities.
As the Chronicle of Higher Ed reported this week, college towns tend to be more liberal than the states they inhabit. In their research, counties that housed flagship universities tended to view the republican candidate less favorably that the state as a whole. Wisconsin-Madison was a particularly good example. The republican candidate won the state by about 1% but lost Dane County (home of University of Wisconsin – Madison) by about 48%. So the take-home here is that college towns lean left, which isn’t really a surprise. But then we also need to keep in mind that those left leanings are coming largely from the university affiliates and residents of the counties, not students themselves, since they typically aren’t registered to vote in the same place they go to college.
But in any case, I don’t exactly take heart at the sight of this data, because those numbers aren’t telling us college campuses are unified, as much as they are telling us that we as universities are alienated from our surroundings, and we as faculty are alienated from our students, which doesn’t feel so great. The Chronicle team generated several other data sets to explore the demographics of the vote across academic cross sections, also considering race and gender.
There are still plenty of lingering questions about what all of this means for us, as educators and academics. The Chronicle of Higher Ed is keeping us up to date with a lot of this in their series A Stunning Upset (my apologies, many of their articles are behind a paywall). Specific questions that I am concerned about, include what this all means for federal funding for research and, more broadly, for universities? Will the changing priorities of the government be reflected in changing priorities of institutions? And what’s in store for students who are still in the process of financing their educations?
I guess we wait and see.
I should also mention, as a mathematician, there is also a lot of interesting conversation going on about the efficacy of polling, and how biased algorithms might have shaped the outcome of the election. Blogger extraordinaire, Cathy O’Neil, has done some particularly great work in the past week discussing some of the data driven pitfalls of utter chaos 2016. Among other things, this election and the journalism surrounding it has reminded me how important it is to understand where numbers come from. An infographic with a needle swinging side-to-side is all well and good when it’s swinging in the proper direction, but when it starts to lean the other way, suddenly I’m forced to ask myself, “Wait, what am I even looking at right now?”