Lan Dinh is a senior studying Data Science. She is responsible for data critique and multimodal elements sections. She also contributes two annotated bibliographies. One on the Supreme Court’s decision potentially favoring white men in admissions and another on gender bias through an analysis from liberal arts colleges, revealing a preference for male applicants.
Meghana Ramineni is a senior studying Data Science and Cognitive Science. She contributed to the introduction, history, methodology, and analysis of the narrative as well as web design. She included three annotated bibliographies regarding CRT, Black Marxism, and social stratification in higher education.
Sarah Son is a senior studying Statistics and Data Science. She was primarily responsible for the data cleaning and visualizations in this project. She also contributed two articles to the annotated bibliography regarding the effects of race, sex, and class in college admissions and worked on the website.
Joshua Chang is a senior studying Data Science. Josh focused on the website design, as well as the theoretical framework and historical context sections of the narrative.
Khankamol Chor Kongrukgreatiyos is a rising senior majoring in Computer Science and Data Science. She compiled resources for the further reading page and designed the website layout as well as its assets. She is also responsible for video editing and finalizing the project deliverable.
David Sanchez is a rising senior studying Data science. He was responsible for the analysis and conclusion of the narrative. He also contributed to editing a couple pages from the website and Annotated bibiliagraphy.
Siqi Huang is a senior studying Data Science. She was responsible for data visualization in this project. She also contributed articles to the annotated bibliography in connection with race, gender bias and also participated in gender narrative section.
The written sources that primarily inspired us to begin this digital humanities project are located in our annotated bibliography. The site that we used to find our data and data dictionary can be found here. Linked are the CSV file and data dictionary.
We used Jupyter Notebook as our primary computing platform and did our data cleaning and data visualizations using Python. Our visualization library of choice was Seaborn. The dataset we selected did not require heavy cleaning as the values and labels were uniform and intuitive. We did, however, drop null values as there were only two of them.