Success as a faculty member requires one thing above all else: a good reputation in your field.
So I agree with the conclusion that the overall standard of faculty is not impressive to students, but it's not because the faculty are not risk takers or innovative - it's because for the most part, the faculty are political animals, not technical ones. They may also be very good technically, but this is an accident and not a feature of the system and untrue more often as not. The cynical students mentioned in the article will likely sniff that out quite quickly whereas, in my experience, the good students are simply more naive. Both types tend to be just as intelligent and hard working, but the cynical students will work on what they want, rather than what the professor wants.
In my experience, yes, including your minor clarification on the word 'good', the full description applies equally to chemistry as philosophy (presumably). But then that makes one wonder: Why are we entrusting this system with taxpayer resources to produce science?
Secondly, "But the university doesn't need to exert any pressure, because it's already filtered out the people who would need to be pressured..." What does this mean for controversial topics, like climate science, where political decisions are made based on 'consensus'?
"But the climate science community doesn't need to exert any pressure to conform to the accepted model, because it's already filtered out the people who would need to be pressured to accept it"?
Equally valid, to replace "climate science" with "cancer research", eg.
As examples, consider the Copenhagen consensus in foundations of quantum mechanics or the (currently being overturned, thanks to machine learning) Frequentist consensus in statistics.
In contrast, Bayesians use probability to represent uncertainty.
I.e., to a Frequentist, it doesn't even make sense to ask the question "what is the probability that the coin is rigged", whereas the Bayesian would come up with a probability distribution for the probability of the coin coming up heads.
 I find that battles over terminology can be the harshest and most recriminating minefields among the intelligent.
Whereas Bayesians believe P(x) is odds at which they would gamble, so to speak, that a particular proposition x is true.
Is that it?
In contrast, Bayesians compute P(null hypothesis is false | prior knowledge).
Similarly, Frequentists compute confidence intervals (say at 5%), which is an interval that represents the set of null hypothesis you can't reject with a 5% p-value cutoff. In contrast, Bayesians compute credible intervals, which represent a region having a 95% probability of containing the true value.
Personally, I'm solidly in the Bayesian camp simply because I can actually understand it. To take an example, consider Bem's "Feeling the Future" paper  which suggests that psychic powers exist. From a Bayesian perspective, I understand exactly how to interpret this - my prior suggests psychic powers are unlikely, and my posterior after reading Bem's paper is only a little different from my prior. I don't know how to interpret his paper from a Frequentist perspective.
 http://www.dbem.ws/FeelingFuture.pdf For background, his statistical methods were fairly good, and more or less the standard of psychology research. If you reject his paper on methodological grounds, you need to reject almost everything.