 To see this consider our simple weather frameworks. Suppose that all I learn is that it is rainy. Do I thereby have some grounds for thinking \(A\) is closer to the truth than \(B\)? I would if I also knew that \(h\)-\(r\)-\(w\)-ese is the language for calculating distances. For then, whatever the truth is, \(A\) makes one fewer mistake than \(B\) makes. \(A\) gets it right on the rain factor, while \(B\) doesn’t, and they must score the same on the other two factors whatever the truth of the matter. But if we switch to \(h\)-\(m\)-\(a\)-ese then \(A\)’s epistemic superiority is no longer guaranteed. If, for example, \(T\) is the truth then \(B\) will be closer to the truth than \(A\). That’s because in the \(h\)-\(m\)-\(a\) framework raininess as such doesn’t count in favor or against the truthlikeness of a proposition.