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Terentianus Maurus, a North African writing probably in the third century a.d., bequeathed to posterity a preface and three polymetric poems: De litteris, De syllabis and De metris. The poems' titles reflect their content, the first two covering the pronunciation of letters and syllables and the third discussing the details of a bewildering array of metres. Unpromising subject matter for poetry? On the contrary. Terentianus Maurus uses this raw material to display his extraordinary poetic skill, while also conveying useful technical information. This paper first examines the programmatic preface to his poems, which is studded with intertextual gems and shines with every kind of literary polish. It then turns to look at passages from the rest of Terentianus' poetry to see how he puts the ideals of his preface into poetic practice. The paper aims to show that Terentianus Maurus is not, or not just, a grammarian, but rather a consummate literary artist in the tradition of learned didactic verse. [ABSTRACT FROM AUTHOR]

In this article we describe a series of computer algorithms that generate prose rhythm data for any digitised corpus of Latin texts. Using these algorithms, we present prose rhythm data for most major extant Latin prose authors from Cato the Elder through the second century a.d. Next we offer a new approach to determining the statistical significance of such data. We show that, while only some Latin authors adhere to the Ciceronian rhythmic canon, every Latin author is 'rhythmical' — they just choose different rhythms. Then we give answers to some particular questions based on our data and statistical approach, focusing on Cicero, Sallust, Tacitus and Pliny the Younger. In addition to providing comprehensive new data on Latin prose rhythm, presenting new results based on that data and confirming certain long-standing beliefs, we hope to make a contribution to a discussion of digital and statistical methodology in the study of Latin prose rhythm and in Classics more generally. The Supplementary Material available online (https://doi.org/10.1017/S0075435819000881) contains an appendix with tables, data and code. This appendix constitutes a static 'version of record' for the data presented in this article, but we expect to continue to update our code and data; updates can be found in the repository of the Classical Language Toolkit (https://github.com/cltk/cltk). [ABSTRACT FROM AUTHOR]