Leviticus 17–26, an ancient law text known as the Holiness Code, prescribes how particular persons are to behave in concrete, everyday situations. The addressees of the law text must revere their parents, respect the elderly, fear God, take care of their fellow, provide for the sojourner, and so on. The sojourner has his own obligations, as do the priests. Even God is said to behave in various ways towards various persons. Thus, the law text forms an intricate web of persons and interactions. There is a growing awareness that ancient law texts were not arbitrary collections of legal paragraphs but articulations of certain world views. The laws were rational in their own respect and were based on the lawgiver’s ethos. However, since the ethical values of the lawgiver rarely—if ever—surface in the text itself, it has proven difficult to grasp with traditional, exegetical methods. This study offers a novel approach to mapping out the ethos of an ancient law text like Leviticus 17–26. By employing social network analysis, the participants and their interactions are mapped to scrutinize the ethical roles embodied by the persons of the law. To accomplish this, the study undertakes meticulous research into both the participants and the interactions of Leviticus 17–26. The book investigates a semi-automatic approach to extracting participant information from a text and offers new methods for analysing Hebrew interactions (realised as verbal predicates) in terms of dynamicity, causation, and agency.

Social network analysis has only occasionally been applied to the analysis of texts. This chapter offers a novel attempt to map the social network of an ancient law text, Leviticus 17–26. Using the participants and their interactions as nodes and edges, respectively, graph-based and feature-based approaches to role discovery are applied to detect clusters of participants. Three clusters emerge, and crucial representatives of each cluster are analysed in depth to suggest the roles obtained by the participants in the network. Finally, the ethos expressed by the network is discussed and related to the major theme of holiness in Leviticus 17–26. The chapter advances existing methods of social network analysis of texts by conceptualizing the network edges as the agency invested by the participants in concrete interactions. Moreover, it is argued that texts exhibit another dimension apart from participants and interactions, namely that of textual structure. Participants and interactions are embedded in a discourse or narrative, and their embeddedness adds important information about their role in the social network as implied by the text.

Social network analysis relies on a mapping of relationships or interactions among persons or groups. Often a particular relationship is assumed (e.g., marriage or trade ties), but all sorts of relationships can be taken into account. One way to deal with social network analysis of texts is to extract all event structures consisting of an actor, undergoer, and event, grammatically realized as subject, (in)direct object, and verbal predicate. Leviticus 17–26 contains 181 different verbs resulting in a complex social network. This prompts the question of how to differentiate between various verbal events in a quantifiable way. The question is addressed by invoking linguistic theories of agency since all events presuppose a certain amount of effort on behalf of the agents. By implication, verbs can be measured according to agency required, that is, amount of force and volition. This chapter develops a framework for capturing agency from verbal events on the basis of the Role and Reference Grammar theory for deriving semantic roles from the lexical aspect of verbs, also known as Aktionsart. It is argued that dynamicity and causation are the two most important verbal features for decoding agency.

The concluding chapter summarizes the findings of the book and evaluates the methods developed and results achieved. A number of potential avenues for further research are suggested including expanding the analysis to other legal corpora of the ancient Near East for the sake of comparing the world views represented by different corpora.

In this paper, I will present a new method of applying open source digital resources for investigating patterns of participants in the legal material of the so-called Holiness Code (Lev 17-26). Although several scholars have tried to explain the internal relationships between the participants in the Holiness Code, the large corpus complicates a systematic and consistent analysis. Developments within computational corpus-linguistics, however, provide new ways of systematic investigations into the discourse of Biblical legal texts. Accordingly, this paper will demonstrate how open source technologies and resources, such as the ETCBC database, text-fabric, R, and Jupyter Notebook, can benefit discourse analysis of the Holiness Code. Using Leviticus 25:23-28 as a case-study, the paper presents a three-step method of creating a semantic map of the participants and their internal relationships. First, semantic role labels are distributed to all participants according to the Role and Reference Grammar theory of the thematic relationship between verb and arguments. Second, all linguistic participant references are tracked and linked to their respective referents. Finally, the formal, linguistic participant tracking and the semantic role labelling are combined to create a semantic map of participants.

In this paper, we present our approach to adding the first layers of syntactic information to an open dataset containing the text of the Samaritan Pentateuch with linguistic annotations. The Samaritan Pentateuch, written in Samaritan Hebrew, is the sacred text of the Samaritan community. The updated dataset now includes boundaries for phrase atoms and phrases. These were added using a parser previously employed to annotate Hebrew and Syriac texts morphologically. While the model predicts the locations of phrase atom and phrase boundaries, its output is not always accurate, so manual corrections were applied. This project highlights the potential of machine learning to enrich ancient texts. The syntactic features developed here can support linguistic research in the humanities and facilitate textual comparisons across ancient corpora—extending beyond morphological analysis to deepen our understanding of textual transmission. Additionally, the corrected data can be incorporated into the existing training set to improve future models.