To my knowledge, it's not discussed explicitly in the wider literature. I'm not a statistician by training though, so my knowledge of the literature is not brilliant.

On the other hand, talking to working Bayesian statisticians about "what do you do if we don't know what the model should be" seems to reliably return answers of broad form "throw that uncertainty into a two-level model, run the update, and let the data tell you which model is correct". Which is the less formal version of what Jaynes is doing here.

This seems to be a reasonable discussion of the same basic material, though in a setting of finitely many models rather than the continuum of p models for Jaynes.