TY - JOUR

T1 - Hidden Value

T2 - Provenance as a Source for Economic and Social History

AU - Rother, Lynn

AU - Mariani, Fabio

AU - Koss, Max

N1 - 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.

PY - 2023/5/25

Y1 - 2023/5/25

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Digitale Methoden

KW - Erbschaft

KW - Gender

KW - Kunstmärkte

KW - Künstliche Intelligenz

KW - Museen

KW - Natural Language Processing

KW - Provenienz

KW - Provenienzdaten

KW - Reichtum

KW - Wertbildung

KW - art

KW - art markets

KW - artificial intelligence

KW - deep learning

KW - digital methods

KW - gender

KW - inheritance

KW - museums

KW - natural language processing

KW - provenance

KW - provenance data

KW - value formation

KW - wealth. Kunst

KW - History

UR - http://www.scopus.com/inward/record.url?scp=85154059822&partnerID=8YFLogxK

U2 - 10.1515/jbwg-2023-0005

DO - 10.1515/jbwg-2023-0005

M3 - Journal articles

VL - 64

SP - 111

EP - 142

JO - Jahrbuch fur Wirtschaftsgeschichte

JF - Jahrbuch fur Wirtschaftsgeschichte

SN - 0075-2800

IS - 1

ER -