I see your point, but those excluded by not being admitted do not have to be the smartest cookies in the jar. Their inclusion in the sample may create enough noise (unexplained variance) to reduce the correlation to trivial levels, even if it is statistically significant. So the devil is in p levels and R squares.
Another point is that if you have very large samples (i.e. 10k+) which is the case with most "scholastic aptitude" testing - almost everything is statistically significant (p < 0.05) due to the sheer sample size, so it is really important to look how much variance in academic performance is being explained by IQ test scores (R square).
The point I am trying to make here is that when we have a situation that a model explains a relatively small percent of variance (say, less than 20%) that indicates that the variables that can potentially explain most (80%) of the variance are not in the equation. This, in turn creates a strong possibility of spuriousness of the observed effects. That is to say, if we introduce those other variables that may potentially explain the hitherto unexplained 80% of the variance, the chances are the effects of the variables already in the equation may be "explained away" i.e. become statistically insignificant or even reverse its sign (i.e. from positive to negative).
So unless someone shows me that IQ test alone explains more than, say, 60 percent of the variance on academic achievement, or that IQ tests together with other variables explain that much variance and IQ still remains significant - I remain unimpressed by all those correlations claims.
IQ testing is not really my field, so I may not be aware of the compelling evidence that is out there in some obscure publications, but I know enough science and ideology disguised as science to smell a rat here.
Wojtek