During the final week of instruction for our class, my grasp of archival historic information, programming and visualization methods grew exponentially. In terms of the literature for the week, we analyzed three interesting readings by Nathan Yau, Tahu Kukutai and John Taylor, and Catherine D’Ignazio and Lauren Klein. The reading that struck me the most was Kukutai and Taylor’s article on “Data Sovereignty for Indigenous Peoples.” Indigeneous data sovereignty is the right of a nation to govern the collection, ownership, and application of data. Yet, indigeious communities have struggled with these data issues based on the generalizations that these communities tend to be poorer and politically weaker than their surrounding neighbors. I learned that data sovereignty is very significant to these communities because it provides research information and policy advocacy to protect the rights of these people, while also promoting the interest of the Infigrnous nations in relations to the data. As a result, I believe that communities native to the land deserve to manipulate and control the collection of their own information. We also worked on various coding methods this week such as customizing histograms, scatterplots and heatmaps. This added knowledge in creating visual representations of our data was very significant for producing visualizations for our final projects that can successfully exhibit clear interpretations of the data for our audience.
Catherine D’Ignazio and Lauren Klein’s reading, “What Gets Counted” may be the most important piece of literature that explains the significance of my project. My project analyzes if gender plays a role in the donation process by differentiating between gender and amount donated. With a focus on the observed limitations of a clear wealth and wage gap for women, D’Ignazio and Klein’s reading relates significantly to my own work. In their article, they discuss the idea of Matrix domination which describes how race, gender, and class intersect to enhance opportunities for some people and constrain opportunities for others. Therefore what is not counted becomes “invisible.” In the case of my project, our data only includes names and donation amounts of the donors. Without considering the clear wage disparities during this time period and the limitations of women in terms of education, labor, and social constructs, then anyone analyzing this data may develop misinterpretations of the dataset. Some may have said that women were less willing to donate to the college, however my research and data findings prove that the constraints on women and affordability concerns were what played a significant role in donation amounts. Therefore, Catherine D’Ignazio and Lauren Klein’s reading provides a background on how datasets can “dominate, discipline, and exclude” important information that may misconstrue the validity of past history. Through my research, I plan to alter those misconceptions and elucidate the clear disparities of wealth between men and women during these time periods.
Sara Costanza-Chock’s “Design Justice: Towards an Intersectionnal Feminist Framework for Design Theoy and Practice” is the second of our readings that relates well to my project. In her work, Costanza-Chock discusses an innovative social movement known as Design Justice, which aims to ensure a more equitable distribution of design’s benefits and burdens. The author discusses that the movement is centered around people who are normally marginalized by design and proposes creative practices to address the challenges the communities face. Marginalized communities encompass more than just race, but also include the limitations women have faced throughout history. With the support of this literature and our data, my project will address the experiences throughout the donation process but also reveal the challenges women faced in terms of acquiring wealth and income. Therefore, Sara Costanza-Chock’s reading will provide a useful framework for my research and interpretation of my data.
The final piece of literature that my project will be based upon is Katie Rawson and Trevor Munoz’s “Against Cleaning.” In their article, Munoz and Rawson discuss that cleaning data implies that the data is “messy” which suggests there should be an underlying order. Therefore, when data is considered messy we then remove certain aspects of a dataset in order to make the data set look “clean.” As an economics major, I have learned that removing variables can contribute to omitted variable bias which in turn diminishes the validity of our results. In terms of our data, they may be several outliers in donation amounts. Some may feel inclined to drop these values because they do not follow a general correlation with the data, however I feel that it is important to include it because these large amounts which I believe are mostly by men will only support the wide gap in income and wealth inequality between men and women. Furthermore, we have discussed several times in class that the names of our dataset are very difficult to either read or construe whether they are male or female. If the focus was not on gender this process would not be an issue, but we cannot just ignore these challenges because differentiating between male and female will be crucial to matching amounts to gender. Ultimately, Rawson and Munoz’s article reminds me that I must analyze and think before I sort any of my data because the results could be altered.