 His first move was to fix two regularity, or normalizing, conditions imposing that no relative frequency \(m/n\) observed in any initial section of a sequence can be negative, and that for every possible n so obtained all the corresponding values of \(m_i\) must add up to one. He then added a requirement of linguistic invariance stating that the inferences made by means of a given rule must be invariant with respect to the language in which the evidence taken into consideration is expressed. In other words, what matters to inductive inferences is the content of evidence statements, not their linguistic form. For a while Salmon thought that these requirements could fulfill the purpose for which they had been proposed. He was able to demonstrate that the normalizing conditions could eliminate a large subclass of Reichenbach’s asymptotic rules (Salmon 1956), and that the criterion of linguistic invariance could do the same with all the methods belonging to Carnap’s continuum, except the one Carnap called the straight rule—alias the rule of induction (Salmon 1961). Among other things, the criterion of linguistic invariance allowed Salmon to suggest an original solution to Goodman’s paradox (Salmon 1963c).