UC Berkeley professor Michael Jordan, a leading researcher in machine learning, has a great reduction of the question "Are your inferences Bayesian or Frequentist?". The reduction is basically "Which term are you varying in the loss function?". He calls this the "decision theoretic perspective" on the debate, and uses this terminology well in keeping with LessWrong interests.
I don't have time to write a top-level post about this (maybe someone else does?), but I quite liked the lecture, and thought I should at least post the link!
The discussion gets much clearer starting at the 10:11 slide, which you can click on and skip to if you like, but I watched the first 10 minutes anyway to get a sense of his general attitude.
Enjoy! I recommend watching while you eat, if it saves you time and the food's not too distracting :)