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Summary of May 7 Libraries In-house Institute session

Posted on May 22, 2018 by Julie Hardesty

The May 7, 2018 IU Libraries’ In-house Institute session, “Inclusivity and Bias in Metadata: Continuing the Conversation” began with a brief recap of two previous Metadata Discussion Group sessions this spring, Facts in Metadata and Bias in Metadata, followed by listening to the NPR CodeSwitch interview about implicit bias that discussed the one-day training that Starbucks employees will receive in response to a racial bias incident in Philadelphia. We then moved to a discussion of ways to address inclusion and bias in metadata standards. In discussing the fact that Library of Congress (LC) copy records are not richly described, a participant suggested leveraging consortiums or institutional affiliations in order to get catalog records.[1] On the other hand LC standards, while conservative and showing bias, have taken a long time to come about and be standardized so it is difficult to imagine re-doing all of that work.

Participants mused on the benefits of accepting the use of vocabularies other than those from LC. Such changes would require thorough and thoughtful planning, documentation, and change implementations in multiple systems. A major concept in this kind of re-thinking of the catalog records we create is to consider the marginalized communities that approach our catalog or are represented by items in our catalog.[2] Placing those communities in the center of our cataloging practice to ensure they can find themselves and what they need in our description ensures that the most marginalized are included in what we do, making all of our records that much more accessible to everyone.

The conversation then turned to translations and providing multiple language versions of cataloging records. All too often catalogers are presented with items about an unfamiliar subject, possibly in an unfamiliar language. Trying to offer translations and multiple language versions helps everyone find these items more easily but requires a lot more cataloging time and the tools are not always well-suited to show multiple language versions of cataloging records. Lack of subject matter familiarity seemed to be an even bigger obstacle. Presented with something about an unfamiliar subject, such as a place or culture, descriptive cataloging can end up too broad, sparse, and unhelpful, leading to catalog records without much detail and easily missed in normal end-user searching and browsing, especially for those end-users who are familiar with the subject. This has the effect of making small or specialized collections that much harder to find. One solution suggested by attending subject librarians was to enlist them in the cataloging process. Contacting a subject librarian with expertise on the subject presented in that item can help provide more detailed and relevant subject terms. It was also suggested that collection managers could help supply controlled vocabulary terms to enhance description or even provide terms that are not part of any controlled vocabulary if those sets of terms are not helpful to describe a collection. The big takeaway from this part of the conversation was that there did seem to be steps we could take in the cataloging process to improve inclusivity of better descriptive terms to provide catalog records that are more accessible.

We ended the session by discussing what next steps we could take to continue this conversation and make positive changes in our cataloging practices to provide more inclusive and accessible catalog records and combat our own implicit biases in our cataloging practice. Metadata Discussion Group sessions will continue to explore these topics. Implicit bias training and active bystander strategies training seem to hold a promise to at least increase awareness if not actually stop biased activity and behavior so we are hopeful that some kind of program can be made available within the Libraries.[3] Additionally, we can create a list of expertise in subject areas for catalogers to reference when encountering unfamiliar subjects to help enhance those records for better discoverability.

All of these ideas and others that we didn’t have time to discuss in this session are gathered in an online spreadsheet from this session: Inclusivity and Bias in Metadata – In-house Institute 2018. All are welcome to add more ideas on how to address inclusion and bias in metadata standards, our application of those standards, how to build transparency into our tools and processes, areas of improvement, and next steps that we can take as a library to provide relevant and inclusive information about our collections for better discoverability and access.

Stay tuned for future posts and scheduled events as we continue to think critically about our work as catalogers and metadata creators.

[1] Editorial note from Jennifer: IU Libraries is a long-time, productive member of the Program for Cooperative Cataloging and has in the past leveraged affiliations with the Big Ten Academic Alliance Library Initiatives in order to purchase ebooks and their associated catalog records.

[2] For additional and really helpful readings on the subject of inclusivity and bias in library practice, see the Digital Library Federation (DLF) Cultural Assessment Working Group’s Annotated Bibliography, particularly the readings in the sections on Collection Publicizing and Discoverability and Metadata and Description Practices: https://osf.io/94pgj/

[3] Jennifer Liss has started an open reading list within IUCAT on Implicit Bias: https://iucat.iu.edu/catalog/myfolders/folders/108239669

Author- Julie Hardesty

Julie Hardesty is the Metadata Analyst for the Indiana University Libraries. Her work focuses on the use of metadata standards and best practices to enhance access to and discoverability of academic online resources. She holds a Masters degree in Information Science and a Master of Arts degree in Art History, both from Indiana University. View all posts by Julie Hardesty

Posted on May 22, 2018 by Author Julie Hardesty Posted in Categories Meeting Notes | Tagged: Tags inclusive metadata, standards bias

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