Events

Applying unseen species models to the survival of culture

Abstract:

Researchers of the past — whether historians, literary scholars or archaeologists — depend on the sources that have stood the test of time. That sample of history is usually far from complete, however. There are numerous reasons for this, such as natural causes (e.g., fires or floods), decisions at the level of archival policy (what do we preserve and what do we not?), and biases in the formation of the archives themselves. Data representing lower classes were long considered less relevant, for example, and thus socioeconomic factors likewise play a role in the survival of sources. In a series of recent experiments, we have explored how statistical methods from ecology can help us identify such gaps and biases in our knowledge. Those methods all find their basis in “Unseen Species Models,” which were were originally developed to estimate the number of unique species in an environment. Just as ecologists try to estimate biodiversity from an incomplete sample, we apply the models to incomplete historical archives to measure the actual cultural diversity. In this talk, we apply unseen species models to three cases. First, we show how these methods can tell us something about the forgotten medieval chivalric literature in Western Europe. We then apply an extension of the method to the historical archives of the Dutch East India Company, to map out the size of its workforce. Finally, we explore a generalization of the unseen species model with which co-variates of loss or absence can be mapped. We apply this extension to a dataset from historical criminology: the police registers of the Amigo prison (1879-1880) in Brussels, and show how the models can give us an estimate of the “dark number” of unapprehended perpetrators as well as the demographic composition of this group.

Bio:

Folgert Karsdorp, PhD, serves as a senior researcher in Computational Humanities and Cultural Evolution at Amsterdam’s Meertens Institute of the Royal Netherlands Academy of Arts and Sciences. His research delves into the intricacies of cultural change, and he employs a range of techniques to quantify cultural diversity and complexity. A pivotal component of his work revolves around understanding and rectifying biases in these quantifications. To support his inquiries, Karsdorp draws from computational models in fields such as Machine Learning, Cultural Evolution, and Ecology. Outside the realm of research, he harbors a keen interest in teaching computer programming, with a special emphasis on its applications in the Humanities. He co-authored the book “Humanities Data Analysis,” published by Princeton University Press, which instructs readers on harnessing the power of Python to analyze data within the Humanities.

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