This is a general issue of masked array (currently/always). The only solution would be to implement __array_function__ probably.
The best solution is probably to have a masked array replacement that does not necessarily live within NumPy itself.

Implementing __array_function__ only to reject certain functions that are known to not work well (or forward them to the known masked versions) may well work. That is a bit harder outside NumPy currently, since we lack clear public API around it.

I am not seeing an issue, so will rename this one. I am not sure how easy/doable it is, but implementing an `array_function which normally just falls back to the builtin NumPy version may well be a good solution.

Note that masked arrays severely lack any manpower to actually work on such changes.

It wouldn't be hard to slightly modify np.append and have it call np.ma.append if the input is masked, but I'm not sure if that's really necessary here, since we already have a function for appending masked arrays. A better option might be to add a warning about using np.append on masked arrays in the documentation.

It will be an uphill battle to convince anyone to add special paths for MA, which is why I am mentioning __array_function__ as the proper way to achieve the same thing.

Adding a note/warning about this to the masked array documentation seems like a good plan though, I could not find one after a brief search, so it is likely missing.