A Corpus Approach to Roman Law Based on Justinian’s Digest

Abstract

Traditional philological methods in Roman legal scholarship such as close reading and strict juristic reasoning have analysed law in extraordinary detail. Such methods, however, have paid less attention to the empirical characteristics of legal texts and occasionally projected an abstract framework onto the sources. The paper presents a series of computer-assisted methods to open new frontiers of inquiry. Using a Python coding environment, we have built a relational database of the Latin text of the Digest, a historical sourcebook of Roman law compiled under the order of Emperor Justinian in 533 CE. Subsequently, we investigated the structure of Roman law by automatically clustering the sections of the Digest according to their linguistic profile. Finally, we explored the characteristics of Roman legal language according to the principles and methods of computational distributional semantics. Our research has discovered an empirical structure of Roman law which arises from the sources themselves and complements the dominant scholarly assumption that Roman law rests on abstract structures. By building and comparing Latin word embeddings models, we were also able to detect a semantic split in words with general and legal sense. These investigations point to a practical focus in Roman law which is consistent with the view that ancient law schools were more interested in training lawyers for practice rather than in philosophical neatness.

Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

“…The analytical research paper [12] sits at the top of the pyramid. It is the prized result of academic scholarship which all too often adopts a narrow view of research and dismisses the work that researchers are able to make public in the first three layers.…”

“…In order to avoid such friction, one could consider publishing in a gold open access format in an online journal where the review process is rapid, but not compromised. The example project has chosen this route by publishing a data paper in JOHD [11] and an MDPI journal for the corresponding research paper [12]. If the four layers of the pyramid are published in relatively quick succession, and if they adhere to similar open research principles, researchers give their work the best chance of making an impact.…”

“…Gathering citation counts for the datasets associated with the data papers was instead altogether unfeasible. The only repository that provides information on citations (Figshare) reportedly indexes the citation count from Dimensions 12 , which, however, does not provide citation counts for datasets (as of the time of this study). We found that, in fact, the count provided by Figshare corresponded to the citation count of the associated data paper by the same title 13 , so that even the few data citations we could gather turned out to be unusable.…”