[qfwfq_]

I'm a PhD student studying gerrymandering, and I think it's cool this is getting written about in the NYT. But, there are very good models of gerrymandering built off of well-informed counterfactual analysis of generalized linear modelling that can provide both easy to understand indicators of gerrymandering and bias, as well as provide an indication of the uncertainty in those measures.

One thing that's been getting on my nerves in the road to the big D(issertation) is the complete lack of value some in the domain place on that uncertainty.

Elections in single member district systems, at the geography where gerrymandering study becomes meaningful, has to be modelled as a Stochastic system. These kinds of "accounting methods" can vary wildly in their estimates from election to election, and almost always fail to really express how uncertain the indicator is.