Note that degree and type of refutation matters a whole lot. Many theories CAN still be applicable to a more restricted set of predictions, or can be valuable in making less precise predictions. “It’s the best we have” isn’t sufficient, but “it’s the best we have AND it’s good enough for X” could be.
There are TONS of models that get proven wrong, but still allowed excellent progress, and still have validity in a subset of cases (and are usually easier to use than the more complete/precise models).
I suspect the phrase “high-precision” is doing a lot of work in your post that I haven’t fully understood. Almost all of your examples don’t require universality or exception-free application (what I take your “high-precision” requirement to mean), only preponderance of utility in many commonly-encountered cases.
For some of them, a very minor caveat “this may be wrong; here are signs that you might be misapplying it” would redeem the theory without changing hardly any behavior.
Note that degree and type of refutation matters a whole lot. Many theories CAN still be applicable to a more restricted set of predictions, or can be valuable in making less precise predictions. “It’s the best we have” isn’t sufficient, but “it’s the best we have AND it’s good enough for X” could be.
There are TONS of models that get proven wrong, but still allowed excellent progress, and still have validity in a subset of cases (and are usually easier to use than the more complete/precise models).
I suspect the phrase “high-precision” is doing a lot of work in your post that I haven’t fully understood. Almost all of your examples don’t require universality or exception-free application (what I take your “high-precision” requirement to mean), only preponderance of utility in many commonly-encountered cases.
For some of them, a very minor caveat “this may be wrong; here are signs that you might be misapplying it” would redeem the theory without changing hardly any behavior.