Neat reference, but I found the re-analysis rather unconvincing. Classification tables are problematic measures of fit, because that is a test of the level at which one turns a dependent variable to 0 or 1, which is independent from the issue of how the independent variables are correlated with the dependent variable. Given the researcher finds all poverty cases were predicted inaccurately, that's so bad it means the test is meaningless, like when you get a p-value of 0.999.

A big book like The Bell Curve (ie, lots of date presented in different ways) is easy to pick off peicemeal, but like A Monetary History of the United States, the bottom line is the thesis: when you control for SES variables, does a g-loaded variable explain various outcomes? I think the answer is still yes. 

I found the re-analysis quite supportive of the H&M work.

But SES/g correlation is a very small bit of the big picture that's been turning around over the past years: it's the inadequancy of the standard social science model and of "political correctness" (better term: politico-demagogical self-interest) often taking over the (sometimes bitter) truth.

Heckman agrees with the big picture, but softens the edge a little. My own take at this is also that monetary units, IQs and g-factors are overrated. There is a forest out there.