I strongly disagree. Bayesian inference is the only known self-consistent formal system for doing science, i.e. updating our belief system about the world based on the current evidence.
Coming to consensus on priors is the same process for arriving at consensus that all scientific inquiry must engage in. Anyone who says frequentist methods somehow more accurately represent an underlying reality are pulling a fast one.
Bayesian research is just more honest about what's already the case.
That's precisely my point. The act of presenting and refining research IS the act of building that consensus.
My statement here is not a novel thought. It's pretty much the modern philosophy of science for over a decade.
> So we need to new research to gather additional evidence.
This is simply data gathering though. Every approach starts here. I'm not sure why you suggest that people using Bayesian approaches to analysis are somehow forbidden from being informed by data (or informing priors by data).
That's exactly the same process folks use when selecting non-bayesian models. They don't spring from absolute truth, they're selected as well.
> If every research paper tried to argue for a particular prior and posterior
Given the replication crisis that's in part due to mis-application of existing models along with a lack of rigor in data collection, having research focus more tightly on the methodology for presenting data and conclusions doesn't seem like a bad outcome at all.
The different people can then go on and do lots of experiments, collect lots of data and update their priors to posteriors. And the guarantee is that as long as each person's prior was not a mathematically weird function, after enough evidence has been collected all these people will have the same posterior function i.e. they will agree [1].
[1] The famous Aumann's agreement theorem https://en.wikipedia.org/wiki/Aumann%27s_agreement_theorem is a related result that you might like to read about.