Encoding Reparative Description: Developing Tools to Analyze Problematic Finding Aids
Over the past few years, more archives and archivists have been working on enhanced description projects that can address past inequities, erasure, or incorrect representations in description. This work has been variously described as reparative description, mindful description, conscious editing, and by other names. Whatever it is called, while this work is typically grounded in slow, relationship-based work within and outside of archives, the identification of problems and addressing of changes can benefit from computational approaches. Stemming from the “ReConnect/ReCollect” project at University of Michigan, which has surveyed the extent and legacy of colonial collections extracted from the Philippines since the late nineteenth century, we report on work to analyze more than two hundred finding aids with the development of Python-based analysis tools. We are not advocating for technical solutions to fundamental problems, but we are interested in showing how such automation can help to expand the project of reparative description. Our presentation will report on work by students and faculty at the University of Michigan School of Information, collections managers and curators across the University, and systems managers at these various institutions worked together to aggregate finding aid metadata, analyze that descriptive information for potentially harmful, outdated, or problematic terminology and tone. We conclude with information about the code that we used, and the potential challenges and benefits of adopting automated approaches in the implementation of reparative description.
