You’d have a point if you said this wasn’t a transparent study. You’re pretty wrong in suggesting it’s worthless, particularly since it spawned follow-up work [1], some of which mirrors the concerns you present here, though none that has yet reached peer review.
> Mentioning Tibet specifically causes model performance to degrade, this must be the result of censorship or intentional sabotage
OP said “the political restrictions are applied at the presentation/UI layer only.” That doesn’t comport with the sort of error where model performance corresponds to specific geographic modifiers in a way other models do not.
Another comment mentioned potential poisoning due to training-data selection. That tracks! This behaviour is weird enough that I don’t currently suspect sabotage. But it is anomalous behavior happening deeper than presentation/UI.