DH Reads

DH Read: “Resistance, Racialized Violence, and Database Design”

In a post on the Sherman Centre for Digital Scholarship’s site, Sarah Whitwell explains how she defines resistance for use in a research database. From the post:

As Matthew Davis explains, a database is useful as a methodological tool because it does not permit ambiguity. This means that all decisions must be documented and justified. To create the schema for my database, I have already made important decisions about what data to extract from my primary documents – the Slave Narrative Collection, the first-person testimony culled from the Ku Klux Klan hearings, and the records of the Freedmen’s Bureau. Extracting some data does not require significant forethought, such as bibliographic information, dates, or geographic locations. Other data, however, require clearly defined keywords and a rigid workflow. When inputting data on incidents of racialized violence, for example, I must decide how to code types of violence… It is also necessary, however, to define resistance. Recently, there has been a proliferation of scholarship on resistance. But scholars have often failed to define resistance in any systematic way. This poses a challenge for creating a database that requires a concrete definition to ensure consistency. Where resistance is loosely defined, it is possible to see it almost everywhere and nowhere. This blog post, then, will outline how I define resistance.

Whitwell goes on to provide a list of keywords that she uses to code methods of resistance in her database. I was intrigued by this post because I tend to think more about the ways that databases can be dehumanizing, especially for groups of people for whom quantification has been used as part of subjugating and dehumanizing historical processes. However, I don’t think that digital and computational tools must necessarily reproduce the ideologies that inspired the original datasets. Historians read against the grain of sources created by those in power to access other perspectives all the time, so the same should be able to be accomplished using a database, too, right? I think that sometimes it requires a little extra creativity, though, and Whitwell’s post shows that. At the same time, as Whitwell points out, it can be tricky to create a database that is not based entirely on an “objective” set of facts and numbers. According to Whitwell, the key is clear definitions:

To demonstrate the utility of my definition, let us briefly consider an example. Lizzie Atkins, a black woman, stole chickens and potatoes from a white household. In an interview with the Federal Writers’ Project, Atkins admitted that she had committed the theft to compensate for her diminished capacity in southern society. Because Atkins clearly stated her intentions, we must consider the theft to be resistance. Had Atkins stolen for the sole purpose of nourishment, however, we might not consider the theft to be resistance. Where intention is not clear, it is necessary to approach with caution. For James Scott, the theft of food may have represented an assertion of the right to subsistence, but it may also just have been about providing nourishment. It is important, therefore, not to assume an act of resistance where intention is unclear.

As I am starting to think about creating a database of my own for a mini computational history project this summer, I will definitely be keeping this blog post in mind.

Read the original Editors’ Choice piece here.

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