I'm sure they would! But there's a pretty strong argument it's a step in the right direction. Actually, this link makes a more modest proposal that papers should report likelihoods rather than p-values. This avoids reported results results depending on priors (which perhaps we don't trust authors to choose well), though a reader can easily impose themselves if they want to.

https://arbital.com/p/likelihoods_not_pvalues/

You still have the option to muck with things by choosing your hypothesis class in a bad way-- nothing can really replace publishing data!

Probably.. but with Bayesian methods at your model/data updates its priors, rather than you effectively embedding your prior beliefs into your models via selectively choosing tests that support them.

It's very easy to let that slip into post hoc justification of your priors.

In my view, "prior" may be a misnomer. There is nothing that I'm aware of in Bayes' theorem to suggest that you have to formulate your priors before gathering or analyzing your data. I would describe priors as constraints that are included in an analysis, to narrow the results based on additional information that you're aware of. Bayes' theorem mainly provides a framework for computing what happens when you do that.

Maybe the prior doesn’t need to be formulated _before_ getting the data, but it needs to be _independent_ of the data.