Counting the Jeremiahs: Machine Learning and the Jeremiah Narratives
Scholars have long debated the redactional history of the prose sections of Jeremiah (chapters 26-45) but no consensus has been reached on the number of redactional layers in the text, the verses that comprise these layers or their sources. This study used a machine learning method to organise the c...
| Main Author: | |
|---|---|
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
SA ePublications
2021
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| In: |
Old Testament essays
Year: 2021, Volume: 34, Issue: 3, Pages: 718-740 |
| Standardized Subjects / Keyword chains: | B
Synonym
/ Textual criticism
/ Bible. Jeremia 26-45
|
| IxTheo Classification: | HB Old Testament |
| Online Access: |
Volltext (kostenfrei) Volltext (kostenfrei) |
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| 245 | 1 | 0 | |a Counting the Jeremiahs |b Machine Learning and the Jeremiah Narratives |c Nicholas J. Campbell |
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| 520 | |a Scholars have long debated the redactional history of the prose sections of Jeremiah (chapters 26-45) but no consensus has been reached on the number of redactional layers in the text, the verses that comprise these layers or their sources. This study used a machine learning method to organise the chapters into sections based upon authorial word choices. The method used pairs of synonyms in a hierarchical clustering algorithm in the statistical program R. The goal of the study was two-fold. First, the division of the text by computerised model was used to analyse the divisions made by three other more traditional critical methods. Second, the validity of the method used in this study and previous synonym-based studies was analysed and critiqued. The conclusion is that this type of analysis can validate findings from other methods but some of the inherent biases and linguistic ambiguities make it dubious as a primary method of investigation for the Hebrew Bible. https://doi.org/10.17159/2312-3621/2021/v34n3a5 | ||
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