Walking into class with this foreign diamond-shaped application staring me in the face from my computer screen, I was terrified that the sort of dated, very Excel-esque interface was going to ruin my night. Don’t get me wrong, it was challenging, and even in the small bits we explored I did feel like I was learning another language, but surprisingly… I LOVED it.
It felt as if I was finally figuring out how to make my questions answerable, and at the very least making the process more efficient. I immediately was thinking of the ways I could have incorporated this tool into my previous work or how I might utilize it in the future. When I was an intern on the HCAC project, one of my tasks was to analyze how subject metadata was being used and make recommendations for how to improve and standardize it. For example, amongst the hundreds of items, I found usage of “Civil Rights,” “Civil rights,” “Civil rights movement,” and “Civil Rights Movement.” While some of the subject standardization was of course about the content and meaning, a good bit of it was about cleaning the metadata. If I had known how to use Open Refine at that point, I think it would have allowed me to not only fix these inconsistencies with greater ease, but it also probably would have helped me find more issues.
I wish we had had more in-class time to play around with it so I could make more mistakes and learn from them, but I think working with my group was way more beneficial than it would have been learning on my own. As it happened, Kris and I both attacked the first name question from different angles which was unintentional at first. In the end, this allowed us all to see whether the same problem could be solved multiple ways. After splitting the full name column by “,” I did a customized word facet and selected each non first name thing that came up (Sir, van, w, Mrs, etc…) After selecting “include” on those things I pressed “invert” and it gave me pretty much exactly what I wanted. Kris got to the same successful end by doing a “Find and Replace” which got rid of all those non first names and replaced them with a space. She then removed all the spaces and had about the same list I did. While I think we were successful, I definitely think I at least need more time to see if I made any little mistakes in my process. However, doing it this way allowed me to go with my instinct and how my brain automatically wanted work AND learn about Kris’s process so I could potentially employ it in the future.
The readings this week were also of interest to me particularly because I like when data histories highlight humanity in unique ways. I think the inclination of many is to believe that data-driven history are somehow less human or centered on storytelling than traditional histories, but for better or worse, a couple of this week’s readings showed how that does not have to be the case. In fact, ethical DH practices demand that we not remove the human from Digital Humanities. Thinking about the implications of digitization on living people was particularly interesting to me, and I felt that that theme was touched on by the Ziegler reading as well as Data Feminism. Ziegler’s discussion of how digitizing sources and rendering them more easily and publicly available may actually be harmful to a person or population was impactful. They cited Brian Naylor and spoke of how data brokers’ categorizations such as “‘single mom struggling in an urban setting’ or ‘people who did not speak English and felt more comfortable speaking in Spanish’ or ‘gamblers’” can be harmful to the people they describe. Thoughtless digitization, or digitization without interpretation or care for community, can result in the creation of very limiting descriptions of human life. In these cases, the data does not represent the whole of the person and so the charge as digital historians becomes, how do we fix that? Data Feminism somewhat presents a solution to that, or at least presents the logical framework behind intentional DH work. Remembering the ethical and humanistic reasons why we are doing this work, keeps us from falling into these harmful pitfalls and invites us to ask deeper and more meaningful questions. I think these discussions encourage Digital Public History because that specific practice necessitates a more intentional involvement with the community being studied.
Per usual, our group got really head-y with our discussion of born digital sources, preservation, and the decisions that go into archival digitization efforts which was very fun. The broader class discussion on the readings’ themes was also engaging and so in the end, I walked away exhausted after my 13 hour day at school, but with a happy brain and far less fear of the diamond on my desktop.
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