VAC 119
Belinda Isaro, Chasing Beauty
Through the form of poetic documentary, I explored the complexities and nuances of being a woman of color. Poetic documentary creates a particular mood and feeling, and lacks a linear narrative resulting in character growth or resolution to a problem. This best reflects the “working title” theme of the project, as the documentary reflects an ongoing exploration of these ideas. Therefore, it is always a work in progress. Through music and poetry, I explore how race and gender intersect with other identities pertaining to sexual orientation, colorism, and ability. These intersecting identities create a harmonious and disharmonious lived experience for an individual. The project draws heavily upon my own personal experiences, as well as current research on intersectionality.
Emily Lauletta, Renegotiating Liminal Spaces: Catholic Nuns as Spiritual and Feminist Activists
Over the past few years at Hollins, I have performed specific case studies of Catholic nuns and their roles as social justice activists within the Catholic Church. In this project, I work to critically analyze the work of two Sister-led social justice organizations; Network and Talitha Kum. Throughout my paper, I discuss how their actions do or do not align or share commonalities with a spiritual activist framework. This particular framework is informed by the work of Gloria Anzaldúa, Womanism, and Indigenous feminism(s).
Prakriti Pandey, Time Series Analysis to Predict COVID Cases in the United States
Predictive modeling can help us quantitatively better understand diseases like COVID-19, aid in decision making, and take preventive measures sooner than we otherwise would. In this research, I predict the evolution of COVID-19 in the United States. Using a response variable of the first developed stage of COVID-19, I apply statistical and mathematical models, including time-series analysis, ARIMA modeling, and best fit modeling, to predict the number of COVID-19 cases at the later stages. The predictions and results show that the predictions remain constant after a few values. The performance of the learning models is examined and values such as the mean absolute error are used to determine the effectiveness of the model. I also discuss several time-series analyses previously performed by other statisticians for various countries using models such as ARIMA and kth moving average. Similar predictions were done annually by the quadratic time-trend model for a different epidemic called Lassa Fever in Liberia in the 2000s. Non-time series regression for COVID in Sao Paulo, Rio de Janeiro, and Manaus was also performed by several researchers using statistical and cluster analysis to find the best explanatory variables for predictive models. In addition, i consider the work by Fokas et al., 2020 which predicts the time evolution of the cumulative number of individuals reported to be COVID infected in a given country