More than a dozen spoken dialogue systems have recently claimed to be "full-duplex," yet the term has been used to describe substantially different capabilities. Existing surveys collapse them onto a single axis (cascaded/end-to-end, or engineered/learned) and miss the distinctions that matter most for builders. We argue that much of this ambiguity is taxonomical: current terminology does not specify where duplex decisions are made, which interaction types are supported, or how a system behaves moment by moment. This paper introduces three complementary frameworks: (i) an L,0-L,3 Architectural Hierarchy that locates where duplex decisions are made; (ii) a T x Ix R Interaction Ontology that specifies the temporal relation, user intent, and required system response for each interaction; and (i) a Decision State Machine(LDLE/LISTEN/SPEAK/WAIT/DUAL) that describes how systems move between states.Across published systems and benchmarks, our audit reveals a realization gap: although many architectures can in principle operate in full-duplex states, their observed behavior remains constrained by the interaction patterns represented in training and evaluation.We identify the open-source-versus-industrial data gap, as well as the still-unrealized goal of L3 representation-level modeling, as key frontiers for future research on full-duplex dialogue. The related material is available at https://github.com/MM-Speech/DuplexSurvey.