But papers don't publish priors. They publish evidence.

"This study provides 5dB of evidence that the pigs do not reactionless flight capabilities, and 300dB of evidence that I don't know how physics works. However, according to my priors, the flying pigs hypothesis still comes out ahead." said no study ever.

In reality a lot of them publish priors dressed up to look like evidence. As an example see Flaxman2020.

It's best to model journals as magazines that publish anything that vaguely resembles science (has equations, charts, citations, claims of data). It doesn't have to actually be science. If you want to get a paper published it's totally normal to write 10,000 lines of C or FORTRAN, claim it models some natural process, publish a paper with the graphs it outputs and call it a day. The code isn't available. The input data isn't available. The assumptions aren't documented properly or at all. And the outputs are unvalidated, therefore not even laying the foundations of being evidence of anything. Doesn't matter, it'll get published, in some fields this is the vast majority of what gets published (e.g. epidemiology).

An informative prior requires good data… it is a misnomer that Bayesians just “make up” priors, they are generally the result of a past experiment. Indeed a prior can encode just a hunch or intuition if you have nothing else, but in practice that will be nearly identical to a uniform prior.

Personally, I always use a uniform prior unless I am able to cite or present the data that led to something else.