Not in this case. This is a good example of how you can go wrong by overcontrolling (or maybe we should chalk this up as an example of how correlations!=causations?)

Suppose the causal model of Genes->Intelligence->Education->Less-Reproduction is true (and there are no other relationships). Then if we regress on Less-Reproduction and include Intelligence & Education as predictors, we discover that after controlling for Education, Intelligence adds no predictive value & explains no variance & is uncorrelated with Less-Reproduction. Sure, of course: all Intelligence is good for is predicting Education, but we already know each individual's Education. This is an interesting and valid result worth further research in our hypothetical world.

Does this mean dysgenics will be false, since the coefficient of Intelligence is estimated at ~0 by our little regression formula? Nope! We can get dysgenics easily: people with high levels of Genes will cause high levels of Intelligence, which will cause high levels of Education, which will cause high levels of Less-Reproduction, which means that their genes will be be selected against and the next generation start with lower Genes. Even though it's all Education's fault for causing Less-Reproduction, it's still going to hammer the Genes.

I don't know if this sort of problem has a widely-known name (IlyaShpitser might know one); I've seen it described in some papers but without a specific term attached, for example, "Let’s Put Garbage-Can Regressions and Garbage-Can Probits Where They Belong":

James Ray (2003a, 2003b) has discussed several ways in which this garbage-can approach to research can go wrong, even in the simplest cases. First, researchers may be operating in a multi-equation system, perhaps with a triangular causal structure. For example, we may have three endogenous variables, with this causal order: y1 → y2 → y3 . If so, then y1 has an indirect impact on y3 (via y2 ), but controlled for y2 , it has no direct impact...Under the appropriate conditions, the estimated coefficient βˆ1 will represent the direct effect of y1 , and that estimate will converge to zero in this case, even though the total effect may be substantial.^1 If a researcher foolishly concludes from this vanishing coefficient that the total effect of y1 is zero, then, of course, a statistical error has been committed. It may be worth knowing for descriptive purposes that a variable like y2 is correlated with the dependent variable, but as Ray says, that does not mean that it belongs in a regression as a control factor.^2

• Ray, J. L. 2003a. "Explaining interstate conflict and war: What should be controlled for?" Presidential address to the Peace Science Society, University of Arizona, Tucson, November 2, 2002.
• Ray, J. L. 2003b. "Constructing multivariate analyses (of dangerous dyads)". Paper prepared for the annual meeting of the Peace Science Society, University of Michigan, Ann Arbor, Michigan, November 13.

I've wondered whether successful "dumb blondes" are actually unintelligent, or pretending

Don't think it matters much here -- the point is that there are guys who prefer not-smart women. If some women feel it necessary to pretend to be stupid, that just reinforces the point.

For having kids with?

I think they would formulate it as "for bearing them kids". Boys, preferably.

Intelligence is correlated with sense of humor

It's also correlated with success, as Lumifer mentioned elsethread. I thought we were talking about the direct effects of intelligence itself -- though, as gwern points out, that doesn't make too much sense.

Everyone I've ever heard talk about the jock/nerd thing was a US nerd.

IME similar things also happen in Italy (with some differences) and Ireland. I'd guess anywhere people are made to study things they never need outside school by bad teachers, they will resent that and dislike people who can learn such things effortlessly and enjoy it. (Of course I wouldn't expect that to happen as much in places like say Finland or Singapore.)