Disjoint Access Parallelism (DAP) stipulates that operations involving disjoint sets of memory words must be able to progress independently, without interfering with each other. In this work we argue towards revising the two decade old wisdom saying that DAP is a binary condition that splits concurrent programs into scalable and non-scalable. We first present situations where DAP algorithms scale poorly, thus showing that not even algorithms that achieve this property provide scalability under all circumstances. Next, we show that algorithms which violate DAP can sometimes achieve the same scalability and performance as their DAP counterparts. We continue to show how by violating DAP and without sacrificing scalability we are able to circumvent three theoretical results showing that DAP is incompatible with other desirable properties of concurrent programs. Finally we introduce a new property called generalized disjoint-access parallelism (GDAP) which estimates how much of an algorithm is DAP. Algorithms having a large DAP part scale similar to DAP algorithms while not being subject to the same impossibility results.