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Language(s):
eng - English
Dates:
2020-04-23
Publication Status:
Published online
Pages:
00:09 h
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Table of Contents:
What Big Data Doesn’t Reveal: Insights from MPIWG’s LoGaRT (Local Gazetteer Research Tools)” by Shih-Pei Chen invites students to consider how to regard or trust data about past disasters. A genre of geographic information known as “local gazetteers” have often been relied upon to write histories about China. When viewed with digital tools, what was once very locally understood information is now viewed via a deeper temporal perspective. How does this kind of historical thinking inform our experiences living day by day during a contemporary disaster? Students and teachers might use this video lecture to discuss how scientific data such as infection rates of one location produced on any given day, could be considered like one gazetteer among many—each providing one limited facet of a much larger phenomena that requires many more perspectives and the privilege of hindsight to comprehend fully. Broader questions to discuss could include: What is data? How are politics relevant to understanding scientific data? What is the relationship between digital technology and scientific knowledge production?
Details:
Shih-Pei Chen is the principle research scholar behind LoGaRT, a set of digital tools developed at MPIWG. In this lecture, she introduces the relevance of using such tools to collect historical disaster data from Chinese local gazetteers. When using Big Data for historical research over a wide geographical and temporal range, scholars should not naively regard all data from various sources and time-periods as flat. Rather, scholars should take into consideration that Chinese local gazetteers, like other sources for history, were produced by different compilers. These human compilers, furthermore, were situated in different geographical regions, and their gazetteers vary in their times of composition and periods covered. This variation means each example text from the local gazetteer genre can exhibit different collection and omission biases. The same problem can be seen in comparisons of COVID-19 data across regions and nations. Making comparisons among the numbers of confirmed COVID-19 cases can actually obscure major gaps between countries that must otherwise be considered: Some countries proactively test for COVID-19 infections in their asymptomatic populations while others primarily conduct tests only after symptoms are presented in patients. These and other testing strategies and criteria affect the numbers of confirmed cases reported by different national authorities.
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