Hidden Value: Provenance as a Source for Economic and Social History
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Authors
Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and socio-economic custody changes of artworks. They promise statistical and comparative insights into social and economic trends and networks. Such analyses, however, necessitate the transformation of provenances from their textual form into structured data. This article first explores some of the analytical avenues aggregate provenance data can offer for transdisciplinary historical research. It then explains in detail the use of deep learning to address natural language processing tasks for transforming provenance text into structured data, such as Sentence Boundary Detection and Span Categorization. To illustrate the potential of this pioneering approach, this article ends with two examples of preliminary analysis of structured provenance data.
| Original language | English |
|---|---|
| Journal | Jahrbuch fur Wirtschaftsgeschichte |
| Volume | 64 |
| Issue number | 1 |
| Pages (from-to) | 111-142 |
| Number of pages | 32 |
| ISSN | 0075-2800 |
| DOIs | |
| Publication status | Published - 25.05.2023 |
Bibliographical note
The authors would like to thank the Art Institute of Chicago for making publicly available and downloadable detailed object data on its collection, including provenance information. We are grateful, in particular, to Amanda Block and Jennifer Cohen. We extend our gratitude to the anonymous peer reviewer and to Liza Weber for her rigorous and incisive editing of multiple versions of this article.
Publisher Copyright:
© 2023 Lynn Rother/Fabio Mariani/Max Koss, published by De Gruyter.