Hidden Value: Provenance as a Source for Economic and Social History

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet


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.
ZeitschriftJahrbuch fur Wirtschaftsgeschichte
Seiten (von - bis)111-142
Anzahl der Seiten32
PublikationsstatusErschienen - 25.05.2023

Bibliographische Notiz

Publisher Copyright:
© 2023 Lynn Rother/Fabio Mariani/Max Koss, published by De Gruyter.


  • Deep Learning, Digitale Methoden, Erbschaft, Gender, Kunstmärkte, Künstliche Intelligenz, Museen, Natural Language Processing, Provenienz, Provenienzdaten, Reichtum, Wertbildung, art, art markets, artificial intelligence, deep learning, digital methods, gender, inheritance, museums, natural language processing, provenance, provenance data, value formation, wealth. Kunst
  • Geschichtswissenschaft