Last year, when I was helping a little bit with the metadata for the manuscripts included on the Italian Paleography website at the Newberry, which I mentioned earlier, I was laughing a little bit at how specific some of the Library of Congress subject headings were and how lacking in specificity were others. As I was trying to categorize some items from Catholic Renaissance Italy, I was seeing just how obvious it was that the subject headings were created by White Anglo-Saxon Protestant Americans. Of course, this bias is relatively very minor compared with others brought up in the chapter we read by Safiya Umoja Noble and in the other writings pointing out the place of bias in the categorization of data. But, I mention it because, despite the fact that all of us who were working on the project were seeing the limitations of using the Library of Congress subject headings, we kept using that categorization because it allowed the project to maintain a certain level of standardization that makes it useful and compatible with other databases. And so, it reveals in a very minor way why it can be difficult to get away from faulty systems of data categorization riddled with problematic ideologies in order to create a totally inclusive space in the digital humanities. It seems like there would need to be a broad and, more or less, simultaneous overhaul of these “standard” systems in order to really make an impact.

Several of the articles we have read have highlighted localized efforts to more inclusively handle data, but I am finding it an unsatisfactory solution and am having difficulty putting my finger on why. I think part of it is that these localized efforts seem to have a way of segregating data. For example, the types of projects dealing with Indigenous archival records that Kimberly Christen addresses or those related to dismantling binaries that Moya Bailey and Reina Gossett mention all seem to segregate the data of these communities rather than opening up an inclusive space in already established and existing spaces. Is this really the best way to breakdown the biases and foster inclusivity? Or are we actually just perpetuating divisions? And, of course, to a certain extent the whole point of having systems of categorization is to be able to break down data into groups to be able to more easily analyze and use it. So, is it really possible to create a totally inclusive and non-segregated system for working with data? These are the questions I’m left pondering as we finish our first two weeks. I don’t expect to come away with answers but perhaps just greater clarity on the problems and the solutions that have been attempted.