Given that the statistician's mantra is that no model is true, it's just that some are useful, I would expect to see a sensitivity analysis of any claim being m

Given that the statistician's mantra is that no model is true, it's just that some are useful, I would expect to see a sensitivity analysis of any claim being made as to the effect of choosing the wrong model on the accuracy of parameter estimates or predictions. Instead, what we get here is mathematical smoke and mirrors, 'cause ultimately what the paper says is that even when you have a very large sample, people might reasonably disagree about how to model that sample and that the data itself won't be sufficient to decide which model is best -- hence "statistical undecidability". Okay, yeah, sure, interesting point, but not exactly shocking to anyone who has ever used any sort of statistics.