Scott Graham
Title: Assistant Professor
University: University of Texas at Austin
Description of Work:
My research is devoted to better understanding the circulation of linguistic patterns and argumentative strategies in biomedical decision systems. Central to this research agenda is a commitment to treating biomedical deliberation and decision-making as complex networks of individuals, scientific instruments, regulatory structures, professional commitments, and economic investments. Within this framework, I argue that to understand the circulation of discourse in complex networks requires new theoretical foundations and methodological approaches consonant with the scope of the task. Subsequently, my research has required me to pursue theoretical advances in rhetorical new materialisms and computational and statistical rhetorics. My most recent line of inquiry has been devoted to leveraging computational methodologies to support a better understanding of conflicts of interest (COI) and associated risks in biomedical publishing and health policy decision-making. As part of this research I have collaborated on the development of new artificial intelligence (AI) system designed to understand and trace COI in biomedical research. This AI system leverages machine learning and high-performance computing to reliably identify and classify individual COIs within author disclosure statements. The resulting analysis indicates that certain types of commercial relationships held by journals or journal parent companies predict higher COI rates among authors in published scholarship. In the next phase of this project, my goal is use computational rhetorical methods to develop new metrics and mechanisms for the evaluation of COI risks in biomedical. This innovation in COI risk evaluation and communication will draw on a shift away from the conceptualization of COI as a problem of individual researchers toward an understanding of COI as network phenomena. The pressing problems of COI and the bias it inculcates stem not from individuals but from the aggregation of COI across networks of researchers and funders.
Socials:
@easyrhetor