Communication non publiée
Back to the Sources : Practicing and Teaching Quantitative History in the 2020s
quantitative history, economic history, historical data, histoire quantitative, histoire économique, données historiques
This paper elaborates on our joint experience of teaching quantitative methods to (mostly) historians since the early 2000s and writing an introductory book on this topic, first in French, then in English, in a revised and expanded version. All along, we have pursued three aims, related to two different types of audience. First, we want to make quantitative methods accessible for all historians—and humanists generally—, especially those who do not think that such methods are “for them,” because they do not enjoy mathematics, or because they study topics that are not traditionally considered as suited to quantification. Second, our intent is to contribute to less routine uses of quantification in the social sciences, by promoting diversity in methods and imagination in categorization schemes—going beyond “the usual suspects” in terms of sources, variables, and calculations. Third, we promote respect for the basic tenets of the historical profession, i.e. principles of source criticism, as the cornerstone of the constitution of data from historical sources. The first part of the paper begins by explaining where we speak from. As practices of quantification differ between countries and sub-disciplines, we first tell a few words about our own experience with quantitative history, in the context of its recent evolutions, since it lost any pretense at dominance in the historical discipline. These trajectories led us to promote constructivist, small-scale, experimental quantitative history. In terms of teaching, this translates into a learning-by-doing focused on the construction and categorization of data from sources. The second and third parts of the paper briefly flesh out the main principles that we promote in our teaching, with examples in and out of economic history and the history of capitalism. The second part addresses the transformation of sources into quantifiable data; the third part discusses data categorization and analysis.