Sponsor: IEEE Central Coast Section
Speaker: Jessica Santana of UCSB
Meeting Date: December 16, 2020
Natural language processing (NLP) holds many promises for understanding the complex relationship between scientific ethics and innovation. “Codes of ethics” emerge from scientific discourse. This study applies natural language processing and semantic network analysis to scientific discourse to discover how ethical norms become ethical codes. The goal of this research is to operationalize ethical codification in scientific discourse for the sociological study of boundary work in science and innovation. We do this by applying community detection to semantic and sociolect networks of scientific discourse and building predictive machine learning models for ethical sociolect community convergence using structured scientific discourse data (e.g. Web of Science) adapted to unstructured scientific discourse data (e.g. emails). In addition to scalable measurement of cultural norms in the production of science, the results of this research will also account for the dynamics of informal discourse captured in online social media streams, will connect linguistic variation with important social outcomes (e.g. innovation), and will reduce biases and data limitations of traditional scientific discourse analysis. Through this research, we ultimately aim to predict when the scientific community will label an innovation an ethical transgression or a scientific achievement. We demonstrate the application of this method in the context of the IEEE and ACM professional software engineering community.
Bio: Jessica Santana is an Assistant Professor in the Technology Management Program at UC Santa Barbara, where she studies the role of networks in innovation and entrepreneurship. Her recent research explored how entrepreneurs use peers and rhetoric to navigate sensemaking and stigma following startup failure. She also investigates the relationship between innovation and ethics in contexts such as synthetic biology and cryptocurrency crowdfunding. Her work is driven by insights from organizational theory, economic sociology, social psychology, and network science. She relies on a variety of methodological approaches, including experimental, statistical, and computational analyses. Her research is informed by her prior experience working in the types of organizations she studies, from Silicon Valley startups to Nicaraguan farming cooperatives. Jessica holds a Ph.D. and M.A. in Sociology from Stanford University and a Master of Information Management and Systems from UC Berkeley’s School of Information, with certification in the Management of Technology from the Haas School of Business.