By Sarah Wu
There is a process in standardization that is oddly satisfying & mechanical. As part CS bro, I find myself working with standardization a lot—the pruning of the spreadsheet, the cutting of excess detail—and it is interesting how hours and hours go into cleaning data. I edit/fix/add tags/words/descriptions is most easily done by the Search and Replace feature; I enter in what I need to change by bulk (for example, changing all tags of Anarchist to Social Justice), and in a second, the spreadsheet updates. Standardization is not a passive process: I am actively deleting comments; I am removing editors in chief so that the list of authors isn’t ridiculously long; I am undercasing uppercased sentences. Cleanliness to the computer scientist is the neat, orderly boxes on an online website; hence, I prune the wildlife from the small press world and turn what is wild into what is tamed.
In Toxic Worlds, my class discussed the way urban cities interpret wildness as something foreign, dangerous—an enemy that has to be resolved, eliminated. Take ragweed for example, it’s scrawny green leaves peeking out of broken concrete sidewalks. The presence of weeds brings about the fear of tripping, the breaking of straight lines, the oversaturation of pollen. It is interesting that a living thing’s desire to grow and expand becomes something dangerous to us, or at least something that is not worth keeping.
It is this uneasy dichotomy—the necessity to remove information in order to make it accessible, useful—that I am most curious about. The intersection in the ethics of data mining, data collecting, and machine learning with what being human/messy/wild is. What do we gain when we dilute the complicated community of books and presses and people into boxes and categorizations on screen? It is growing increasingly normal to see people as data; we as a society are used to our apps and gizmos and gadgets, turning our lives from full-fledged experiences into numbers and data points (your mindfulness app: how happy do you feel today? rate from 1-5), but I wonder if this simplification of data only encourages us to be further detached from the database we are recording and keeping.
For this reason, a real importance (or even necessity) for the methodology that is used when we standardize. As much as turning objects into data shortens truths and segments lives, data also brings about resources, change, visibility; how else can a press survive, if not for marketing and being shown on the internet when a curious reader searches up their name? Standardizing stops seeming like pruning when complexity is placed into it: how can we interact with the presses themselves? What spaces do we allow for complexity to form, in the description of the press and the way they present themselves? How can we immerse ourselves in the mission statements of the objects we work with? These questions allow, I believe, a chance to humanize data into something that can change and grow—something that is still alive.
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