For the final project, my group used Timeline JS and Voyant to delve into how affirmative action has been discussed throughout history at Amherst College, Hamilton College, Vassar College, and Washington and Lee University. I was the one who suggested this topic. In the wake of the recent Supreme Court decision against affirmative action, I thought it was important for us to situate the decision in a broader historical context in order to move forward. I thank my team members for being receptive to my idea and helping me develop concrete research questions.
While doing my preliminary research, I learned a lot about the major Supreme Court decisions on affirmative action, including California v. Bakke (1978), Hopwood v. Texas (1996), Gratz v. Bollinger (2003), Grutter v. Bollinger (2003), and Fisher v. UT Austin (2013). I was able to understand how they differed from and/or built upon each other. After this, I documented articles from The Amherst Student that included the term “affirmative action” and converted them into plain text files. This was an extremely time-consuming process that required great attention to detail. I wonder if I could have done it more efficiently. Nevertheless, this allowed me to experience what it is like to be a historian whose research involves digitized materials. Based on the articles I had documented, I created a timeline of events at Amherst College that were related to affirmative action. For example, I learned that Amherst had filed a joint amicus brief in favor of affirmative action in all three most recent Supreme Court cases.
Apart from the timeline, I also used Voyant to analyze term frequencies in the newspaper articles my team members and I had documented. I was in charge of three visualizations. First, I visualized the frequency of “affirmative action.” Second, I visualized the frequency of different racial groups, including “Black,” “white,” “Asian,” “Hispanic/Latinx,” and “Indigenous/Native.” Third, I visualized the frequency of different factors in admission decisions, including “race,” “class,” “gender,” “scholarship/financial aid,” “legacy” and “athletics.” Although I tried my best to come up with the most logical interpretation for each of these graphs, I felt unsure about my conclusion at times. Some results I just could not explain. However, I do not think this sense of uncertainty that I felt is bad. It simply shows that the distant reading of texts through a digital tool needs to be followed by a close reading of each text and further research in general.
Another limitation of this project was its duration. Because this project was quite fast-paced, it sometimes felt like a hackathon. I was not always able to proofread or double check what I had done. However, the pace of the project taught me the importance of teamwork. I learned to communicate better and delegate my work better because that was the only way I could finish my work in time. I would like to thank my team members for their hard work and open minds.
