Call for papers
The deadline for submissions was 31 January 2025. This call is now closed.
Themed issue description
Humanities scholars have long grappled with the challenge of incomplete historical records and datasets. This issue, known in German scholarship as Überlieferungschance, permeates various disciplines studying the past, where available data often represents only a fraction of the original historical population. Imperfect registration, survival rates, and inherent biases in preservation result in potentially skewed historical narratives, exemplifying a form of 'survivorship bias' in our understanding of the past.
The challenge of missing data extends beyond mere gaps in the historical record. Some types of information are inherently less likely to be captured by physical artifacts such as books or paintings, leading to systematic underrepresentation of certain aspects of historical societies. This bias in material preservation compounds the already complex issue of historical data incompleteness.
Recent years have seen a growing interest in quantifying and addressing these gaps in our historical knowledge. Innovative approaches, such as the application of unseen species models from ecology, are offering new ways to estimate the extent of lost or forgotten data. These methods have found surprising applications across diverse domains in the humanities, from medieval literature to early modern book collections, and from studies of historical professions to criminology.
However, it is crucial to recognize that statistical methods alone cannot fully rectify historical biases. They must be used in conjunction with critical historical analysis, interdisciplinary collaboration, and a commitment to amplifying marginalised voices. This themed issue aims to explore not only the multifaceted challenge of missing data in the humanities and the novel computational methods being developed to address it, but also how these quantitative approaches can be integrated with qualitative methods to provide a more comprehensive and equitable view of historical societies.
Topics of interest
Submissions may address, but are not limited to, the following topics:
- Theoretical approaches to conceptualising and quantifying missing data in humanities datasets
- Adaptations of population estimation models (e.g., unseen species models) to humanities contexts
- Case studies demonstrating innovative methods for estimating historical population sizes
- Critical reflections on the implications of missing data for historical interpretation
- Computational approaches to identifying and mitigating survivorship bias in cultural heritage studies
- Ethical considerations in reconstructing or estimating missing historical information
- The impact of digital humanities techniques on our understanding of historical data gaps
- Methodological reflections on the limitations and potential of quantitative approaches to missing data
- Analyses of biases in material preservation and their impact on historical understanding.
We welcome submissions from scholars across the humanities, including but not limited to history, literature, archaeology, art history, and cultural studies, as well as from computer scientists, mathematicians, statisticians or any other relevant field working on humanities-related projects.
Article types
Authors can choose from several different article types for their contribution to this themed issue - see the Preparing your materials page for the full list of article types available.
If the article type selected is marked as 'by invitation/enquiry only', author should contact the Guest Editors with a pre-submission enquiry.
Submission process
Contacts
If you have questions about this themed issue, please reach out to the Guest Editors:
- Folgert Karsdorp, Meertens Institute, The Netherlands - folgert.karsdorp@meertens.knaw.nl
- Mike Kestemont, University of Antwerp, Belgium - mike.kestemont@uantwerp.be
- Zoe LeBlanc, University of Illinois, USA - zleblanc@illinois.edu
For any questions relating to editorial policy or the submission process, please contact the journal’s Editorial Office at chr@cambridge.org.