A Different History Manifesto: Data Need Not Be “Big” or “Clean”

Clare H. Crowston, University of Illinois at Urbana-Champaign
Claire Lemercier, Centre National de la Recherche Scientifique (CNRS)

This paper presents principles that are put forward in Claire Lemercier and Claire Zalc’s book "Quantitative Methods in the Humanities. An Introduction." It illustrates their virtues and added value in an ongoing research by Crowston and Lemercier (with Steven L. Kaplan) on the history of apprenticeship in eighteenth- nineteenth-century France. It makes the following points: 1/ “data” can be produced from any type of historical source; quantification is not limited to “big” issues (macro rather than micro, economic rather than cultural, etc.). The quantification of a few hundred court decisions can shed light on the very gendered question of how different types of work were valued; 2/ we should focus more energy on discussing the construction of data from sources, and get rid of the metaphor of “data cleaning.” Weird data, missing data, hard-to-categorize data can offer the best insights to the past and quantification can deal with it. Quantitatively reading past statistics on the workforce against the grain sheds a new light on what they ostensibly measured and what they ignored or hid; 3/ data categorization is not necessarily violence done to the sources, if it is done in the context of a specific research question, not on the basis of abstract “ontologies”; for example, experimenting with many ways of categorizing past occupations yields more interesting results than projecting backwards a generic notion of “skills.” Fortunately, many practitioners of social science history already use those principles. They are however at odds with current fashions in economic and digital history–fashions that are supported or promoted by many funding agencies. In this context, we consider it useful to make our principles explicit and to firmly reaffirm why they matter and how they can produce relevant research.

No extended abstract or paper available

 Presented in Session 49. The Data of Labor History