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- Creators: Department of Information Systems
- Creators: Computer Science and Engineering Program
This thesis includes three separate documents: a) a comprehensive document detailing the methods and analysis of the creative factors tied to series success, b) an hour long pilot script based on this data, and c) an industry-standard pitch deck for a TV show created with data insights. In a larger sense, the aim of this study is to take the first steps in remedying information asymmetry between streaming services and content creators. If streaming services were more transparent with their data and communicated to their creators what has been proven to work in the past, showrunners and staff writers could have a new tool to increase the competitiveness of their series and aid in show renewal each year.
Note: This work of creative scholarship is rooted in collaboration between three female artist-scholars: Carly Bates, Raji Ganesan, and Allyson Yoder. Working from a common intersectional, feminist framework, we served as artistic co-directors of each other’s solo pieces and co-producers of Negotiations, in which we share these pieces in relationship to each other. Thus, Negotiations is not a showcase of three individual works, but rather a conversation among three voices. As collaborators, we have been uncompromising in the pursuit of our own unique inquiries and voices, and each of our works of creative scholarship stand alone. However, we believe that all of the parts are best understood in relationship to each other, and to the whole. For this reason, we have chosen to cross-reference our thesis documents.
French Vanilla: An Exploration of Biracial Identity Through Narrative Performance by Carly Bates
Deep roots, shared fruits: Emergent creative process and the ecology of solo performance through “Dress in Something Plain and Dark” by Allyson Yoder
Bhairavi: A Performance-Investigation of Belonging and Dis-Belonging in Diaspora
Communities by Raji Ganesan
Created predictive models using R to determine significant variables that help determine whether someone will default on their loans using a data set of almost 900,000 loan applicants.