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

Abstract: 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.

Angaben zum Objekt

• Weitere Titel: Stille Reserven: Provenienzen als Quelle für Wirtschafts- und Sozialgeschichte
• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Hidden Value: Provenance as a Source for Economic and Social History ; volume:64 ; number:1 ; year:2023 ; pages:111-142 ; extent:32 ; Jahrbuch für Wirtschaftsgeschichte ; 64, Heft 1 (2023), 111-142 (gesamt 32)

Beteiligte, Orts- und Zeitangaben

• Urheber: Rother, Lynn ; Mariani, Fabio ; Koss, Max

Weitere Informationen

• DOI: 10.1515/jbwg-2023-0005
• URN: urn:nbn:de:101:1-2023041414092909209915
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:57 MESZ