Frequentism will also not work well unless you sneak in strong assumptions.

You can get frequentism to work well by its own lights by throwing away information. The canonical example here would be the Mann-Whitney U test. Even if the prior info and data are both too sparse to indicate an adequate sampling distribution/data model, this test will still work (for frequentist values of "work").