Regarding (1), with significance testing the burden of supplying assumptions is not placed on the reader. The assumptions are implicitly built into the NHST rather than explicitly built into the prior.
As for each study becoming a meta-study, that's silly. This is indeed the chewbacca defense. Rather, each empirical study provides Bayes factors which the reader can then use to update their posteriors.
Regarding (2), obviously not every number is a measurement. In Bayesian stats, numbers representing probabilities are quite explicitly opinions. They are meaningful on a ratio scale, and are even asymptotically known to be correct. But they aren't measurements.
(They are correct if your priors are absolutely continuous w.r.t. reality. If you hold a religious belief so strong that evidence can't change it ("100% certainty"), that's not an absolutely continuous prior.)
As for each study becoming a meta-study, that's silly. This is indeed the chewbacca defense. Rather, each empirical study provides Bayes factors which the reader can then use to update their posteriors.
Regarding (2), obviously not every number is a measurement. In Bayesian stats, numbers representing probabilities are quite explicitly opinions. They are meaningful on a ratio scale, and are even asymptotically known to be correct. But they aren't measurements.
(They are correct if your priors are absolutely continuous w.r.t. reality. If you hold a religious belief so strong that evidence can't change it ("100% certainty"), that's not an absolutely continuous prior.)