New Document: “Guiding Principles for Reparative Description at NARA”
From a Blog Post by Archivist of the United States, David Ferriero:
In keeping with the recommendations from my Task Force on Racism, NARA chartered the Reparative Description and Digitization Working Group in July of 2021. Since then, the Working Group has been reviewing guidance, standards, and processes relating to reparative archival description as well as benchmarking the work of peer institutions. Yale University Library notes that reparative archival description “aims to remediate or contextualize potentially outdated or harmful language used in archival description and to create archival description that is accurate, inclusive, and community-centered.” Recently, the Working Group developed Guiding Principles for Reparative Description at NARA.
These principles will guide the efforts of the Working Group as they begin to draft agency guidance for identifying and updating harmful language in current Catalog descriptions and authority records, and for agency-wide reparative descriptive practices going forward. NARA’s reparative description efforts are in keeping with the efforts of numerous other institutions in the archival community. Library and Archives Canada addresses reparative description in action item 17 of their Indigenous Heritage Action Plan.
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Filed under: Academic Libraries, Archives and Special Collections, Digital Preservation, Libraries, News
About Gary Price
Gary Price (firstname.lastname@example.org) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.