I don't think this paper teaches the difference between Bayesianism and Frequentism (though in fairness to the author, that may not be the goal). The paper says Bayesianism is 'aggressive' and 'subjective' while Frequentism is 'cautious' and aims for 'universally acceptable' conclusions. I wouldn't characterize them that way. Frequentism is also subjective (
http://lesswrong.com/lw/1gc/frequentist_statistics_are_frequ...) and Bayesianism can be quite cautious. To me, the real difference in Bayesian and Frequentist methods are the types of questions they aim to answer. Bayesian methods try to answer the question of "What is the probability of the model" given some observed data. Frequentist methods try to answer the question of "What is the probability of the observed data" given an assumed model. In this sense, they are somewhat complementary, which why many practicing statisticians don't actually care about the Bayesianism-Frequentism debate.
I am happy that Bayesian methods are becoming more popular, though, since the questions we actually care about ("what is the probability of the model given the data?") are often best suited by Bayesian methods.
Also, as a physicist I'm disappointed that the paper asserts that physicists disdained statistics in 1903. For the most part, statistical mechanics was developed in the late 1800s! (https://en.wikipedia.org/wiki/Statistical_mechanics#History)