 When \(p\) is true in all cases, but also when \(p\) is false in all cases, there is maximal distance (as opposed to maximal closeness) between \(p\) and the situation \(z\) that \(p\) is supposed to model. By contrast, maximum closeness is equivalent to the precise modelling of \(z\) at the agreed level of abstraction or descriptive adequacy. Maximal distance in the direction of truth will result in \(p\) being true in all cases in which case \(p = \mathord{\top}\) and is minimally informative. Similarly, maximal distance in the direction of falsity results in \(p\) being false in all cases (all possible situations or probability 0) in which case \(p = \mathord{\perp}\) and is minimally informative also. The important difference here is that any distance in this direction is distance bereft of strongly semantic information entirely. This is on account of distance in the direction of “the false” violating the factivity condition on strongly semantic information.