Installing Order, the sociology of science and technology blog, has a request – can you identify scholarly work about unresolved scientific controversies?
I need your help: anybody know a few research papers or a book specifically about unresolved controversies? It would be terrific if there was some conceptualization, or even a functional analysis of the manifest and latent consequences of unresolved controversies. In fact, it would be amazing to see research on “intentionally unresolved controversies.”
My hunch is that they should be rare because writers probably want to focus on narrative with clear stories. Anthropology is full of unresolved controversies, so maybe focusing on the writing surrounding Napoleon Chagnon might be helpful.
What would you suggest?
John Levi-Martin has written a comment on a recent paper by Guo, Li, Wang, Cai, and Duncan claiming that the social contagion of binge drinking associated with a medium genetic propensity. Levi-Martin claims that GLWCD having simply misread their data:
Guo, Li, Wang, Cai and Duncan (2015) recently claimed to have provided evidence for ageneral theory of gene-environment interaction. The theory holds that those who are labelled as having high or low genetic propensity to alcohol use will be unresponsive to environmental factors that predict binge-drinking among those of moderate propensity. They actually demonstrate evidence against their theory, but do not seem to have understood this.
This is consequential because of the way that choose to examine their data. Althoughthe verbal description of the swing theory here refers to the comparison of magnitudes (“more likely”), the methods used by GLWCD involve successive tests of the null hypothesis across three subsets formed by partitioning the sample by level of what is termed genetic propensity. If we denote these three subsets L, M and H, standing for low, medium and high propensity, then, for the kth predictor, they estimate three slopes, bLk, bMk, and bHk. Because the swing theory does not require that any particular predictor have an effect, but only that if it does, it does not in the extreme propensity tiers, this theory holds that for any k, bLk≈bHk≈ 0.
People have been having meltdowns over polls, but I’m a bit more optimistic. When you look at what social science has to say about elections, it did ok last week. I am going to avoid poll aggregators like Nate Silver because they don’t fully disclose what they do and they appear to insert ad hoc adjustments. Not horrible, but I’ll focus on what I can see:
- Nominations: The Party Decides model is the standard. Basically, the idea is that party elites choose the nominee, who is then confirmed by the voters. It got the Democratic nomination right but completely flubbed the GOP nomination. Grade: C+.
- The “fundamentals” of the two party vote: This old and trusty model is a regression between two party vote share and recent economic conditions. Most versions of this model predicted a slim victory for the incumbent party. The figure above is from Seth Masket, who showed that Clinton 2 got almost exactly what the model predicted. Grade: A
- Polling: Averaged out, the poll averages before the election showed Clinton 2 getting +3.3 more points than Trump. She is probably getting about %.6 more than Trump. So the polls were off by about 2.7%. That’s within the margin of error for most polls. I’d say that’s a win. The polls, though, inflated the Johnson vote. Grade: B+.
- Campaigns don’t matter theory: Clinton 2 outspent, out organized, and out advertised Trump (except in the upper midwest) and got the same result as a “fundamentals” model would predict. This supports the view that campaigning has a marginal effect in high information races. Grade: A.
But what about the Electoral College? Contrary to what some folks may think, this is a lot harder to predict because state level polls produce worse results in general. This is why poll aggregators have to tweak the models a lot to get Electoral College forecasts and why they are often off. Also, the Electoral College is designed to magnify small shifts in opinion. A tiny shift in, say, Florida could move your Electoral College total by about 5%. Very unstable. That’s why a lot of academic steer clear of predicting state level results. All I’ll say is that you should take these with a grain of salt.