There is a long tradition in syntax textbooks whereby the early sections (or chapters) present a particular ‘take’ on the key properties of human language (e.g., its unboundedness, the fact that speakers can pass intuitive and more or less definitive judgments on the well-formedness as stand-alone sentences of arbitrary strings of words, and so on), followed by an overview of fundamental data structure that are said to best capture these properties, followed by a series of chapters applying those data structures to a range of natural language data. Along the way, technical refinements are introduced, attempts are made to formalize the often informal statements of descriptive machinery given at the outset to jump-start the discussion, and the discussion increasingly becomes focused on the content of the theoretical framework, with natural language data used as points of departure for exploring that content. Every such framework seems to have its own ‘set piece’ examples which demonstrate its explanatory reach, its ability to capture apparently profound generalizations with a minimal number of additional assumptions. The virtues of this narrative organization are obvious: the point of any science is to capture the behavior of its objects of interest as parsimoniously as possible, which in turn requires an analytic toolkit to capture the generalizations that represent that science's discoveries about its domain of inquiry. In order to say anything useful about that domain, students must first acquire a basic working familiarity with those conceptual tools by applying them to simpler phenomena (typically using optimally simple or idealized data sets) and progressively refine and expand their mastery of the framework by tackling increasingly challenging or even open-ended problems. So far, so good.

But this kind of storyline faces a certain kind of risk: the text becomes in effect a kind of recipe book of stock analyses, with large chunks of the thinking that went into these analyses presented as faits accomplis, which students are expected to internalize and extend to new data. The result is a heavily ‘top-down’ presentation of syntax, making it a matter of mastering a typically complex set of technical tools, specialized notations, and axioms.