All poetic forms come from somewhere. Prosodic templates can be copied for generations, altered by individuals, imported from foreign traditions, or fundamentally changed under the pressures of language evolution. Yet, these relationships are notoriously difficult to trace across languages and times. This article introduces an unsupervised method for detecting structural similarities in poems using local sequence alignment. The method relies on encoding poetic texts as strings of prosodic features using a four-letter alphabet; these sequences are then aligned to derive a distance measure based on weighted symbol (mis)matches. Local alignment allows poems to be clustered according to emergent properties of their underlying prosodic patterns. We evaluate method performance on a meter recognition tasks against strong baselines and show its potential for cross-lingual and historical research using three short case studies: (1) mutations in quantitative meter in classical Latin, (2) European diffusion of the Renaissance hendecasyllable and (3) comparative alignment of modern accentual-syllabic meters in 18–19th century Czech, German and Russian. We release an implementation of the algorithm as a Python package with an open license.

While the indirect evidence suggests that already in the early scholastic period the literary production based on records of oral teaching (so-called reportationes) was not uncommon, there are very few sources commenting on the practice. This article details the design of a study applying stylometric techniques of authorship attribution to a collection developed from reportationes – Stephen Langton’s Quaestiones Theologiae – aiming to uncover layers of editorial work and thus validate some hypotheses regarding the collection’s formation. Following Camps, Clérice, and Pinche (2021), I discuss the implementation of an HTR pipeline and stylometric analysis based on the most frequent words, POS tags, and pseudo-affixes. The proposed study will offer two methodological gains relevant to computational research on the scholastic tradition: it will directly compare performance on manually composed and automatically extracted data, and it will test the validity of transformer-based OCR and automated transcription alignment for workflows applied to scholastic Latin corpora. If successful, this study will provide an easily reusable template for the exploratory analysis of collaborative literary production stemming from medieval universities.