Abstract
This project applies structural topic modeling (STM) and natural language processing (NLP) to strengthen structural mitigation in capital defense. By analyzing unstructured narrative records from child death reviews, STM identifies hidden thematic patterns that illustrate how exposure to poverty, violence, systemic neglect, and environmental disadvantage shapes developmental trajectories relevant to culpability and sentencing. This approach generates objective, place-based, legally admissible evidence consistent with Ohio law and ABA mitigation standards.
Dr. Gia Elise Barboza-Salerno
Director
I am an interdisciplinary scholar whose research integrates law, public health, social work, and advanced statistics to study childhood adversity, violence-related harm, and neighborhood-level inequities. I lead the ISSUES Lab at Ohio State, translating spatial and legal analysis into policy and practice.