I don't think very many practitioners share your view that regressions are meant to be simplistic first steps on the path to more sophisticated analyses or expe

I don't think very many practitioners share your view that regressions are meant to be simplistic first steps on the path to more sophisticated analyses or experiments. If that were true, then I would definitely agree with you that we can forgive most of the shortcommings of frequentist regressions. In practice and in academia, though, I've only ever experienced researchers who see regressions as the main result, and they intend on exclusively using regression models even before they have any sense of what the data is or what the challenges will be. In a lot of cases, they even go out of their way to build frameworks that hard-code in the assumption that whatever model is used must be representable as a set of coefficients and their standard errors. It's painful to see it happen: people have been using regression models for so long (due to the easy manipulation of what the results "mean") that they can't even imagine any other way of doing things. Even in cases where someone might want to earnestly prioritize a move to a more accurate or more flexible modeling framework, they often can't because decades of legacy code has been tailored to work solely with coefficient-based regression models. The software development cost of switching would be so high that they would rather just find excuses to hide the model's inaccuracy and "sell" the results than to bite the bullet and invest in systems where the results are actually statistically rigorous.