Anyhow, because Bayes' theorem can be split up into parts like this, research papers don't have to rely on priors! Each paper could just gather some evidence, and then report the likelihood ratio - P(evidence | hypothesis)/P(evidence).
That's not true in general.
Fair enough. Can I take your point to be "when things get super complicated, sometimes you can make conceptual progress only by not worrying about keeping track of everything?" The only trouble is that once you stop keeping track of probability/significance, it becomes difficult to pick it up again in the future - you'd need to gather additional evidence in a better-understood way to check what's going on. Actually, that's a good analogy for hypothesis generation, with the "difficult to keep track of" stuff becoming the problem of uncertain priors.
http://xkcd.com/1132/
Is this a fair representation of frequentists versus bayesians? I feel like every time the topic comes up, 'Bayesian statistics' is an applause light for me, and I'm not sure why I'm supposed to be applauding.