The reason that paradigm shifts are susceptible to deductive approaches is that they are a form of out-of-distribution prediction, which the external knowledge and conceptual constraints associated with deductive reasoning partially inoculate against. For example, conservation of energy is expected to be true regardless of the paradigm, but an inductive bias that is sensitive to mean energy of a trajectory is not. Similarly, a contrast difference in the cell wall from a staining experiment is expected to be informative regardless of the paradigm, but an inductive bias that is sensitive to the staining hues of a particular vendor is not. Developing models that are capable of prediction under multiple paradigms is indistinguishable from developing models that are capable of mapping distinct (and potentially overlapping) distributions of features (x) and values (y).