def printTheNews(science, ideology):

    if science.getTheme() not in ideology.keys():

        return

    print(science.getTheme(), "says", ideology[science.getTheme()])

A more general observation that I'm sure has been stated many times but clicked for me while reading this: Once you condition on the output of a prediction process, correlations are residuals. Positive/negative/zero coefficients then map not to good/bad/irrelevant but to underrated/overrated/valued accurately.

("Which college a student attends" is the output of a prediction process insofar as diff students attend the most selective college that accepts them and colleges differ only in their admission cutoffs on a common scoring function, I think).

Here's an argument for why the study's conclusions are unsupported.

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Suppose that there are lots of things that go into predicting what makes a student successful. There's ACT score, and GPA, and leadership, and race, and socioeconomic status, and countless other things.

Now, suppose colleges have tried to figure out the weightings for each of those factors, and shared their results with each other.  They all compute "success scores" for each student.

Harvard takes the top 1000 applicants by score.  MIT takes the next 1000.  Princeton takes the third 1000.  And so on.

So, what happens when you run a regression to predict success from ACT/GPA/etc, while controlling for school?

Well, if the formula is correct, nothing is significant!

Consider Princeton.  All its success scores are, say, between +2.04 (Z-score) and +2.02, because it takes a specific thin slice of the population.  That means that all the students are roughly equal.  So if you find a student with a higher ACT score, he's probably got a lower GPA.  Because, if he was that high in both, he'd be higher than +2.04 overall and wind up at Harvard instead of Princeton.

In other words, NOTHING correlates to success, controlling for school, if colleges are good enough at predicting who will succeed.

Sure, there's a small amount of slack, between +2.02 and +2.04, but it's nowhere near enough to produce statistically significant evidence that any factor is important.  Almost 100% of the variance is between schools, not within schools. 

So that leaves noise.  Any coefficients you find that are non-zero are probably just random artifacts.  

Or ... they are systematic errors in how schools evaluate students.

In this particular study, they found that controlling for school, GPA was important to success but ACT score was not.

Well, all that means is that colleges are not weighting GPA highly enough.  It does NOT mean that GPA is more important than ACT score, or any other factor -- only that GPA is more important *after you account for the college's choice in whom to admit*.  It could be that the colleges are giving GPA/ACT a 1:15 ratio, and it should be only 1:10 instead.  In other words, ACT could still be hugely more important than GPA, but the schools are making it a little TOO huge.

Even if everything in the study is correct, I would argue they misunderstood what they were measuring, and what the results mean.  They only mean colleges are underestimating GPA relative to ACT, not that GPA is more important than ACT.

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Here's an analogy:

A store will only let you in if you have exactly $1000 worth of large bills in your wallet.  An academic study measures how much stuff you get based on all the money in your wallet, including small bills.  Since everyone has exactly $1000 in large bills, the regression can't deal with those, and it finds that 100% of the differences in success come from small bills.

That doesn't mean that large bills don't matter!  It means that large bills don't matter given that you got admission to the store.  Large bills DO matter, because otherwise you wouldn't have gotten in!

Similarly, this study's results don't mean that ACT doesn't matter.  They mean that ACT doesn't matter given that you got admission to the college.  If college admission criteria include ACT, then ACT does matter, because otherwise you wouldn't have gotten in!

I'm not very familiar with academia, but have you considered sending this to the authors of the paper to a) see if there are any mistakes you made and b) help them avoid similar errors in the future? 
But I acknowledge that this could lead to a long email exchange that you may not want.