Challenging Classification Bias with Linked Data

Authors

Juliya Borie, University of Toronto
F. Tim Knight, Osgoode Hall Law School of York UniversityFollow
Jordan Hale, University of Toronto

Document Type

Conference Proceeding

Publication Date

5-3-2016

Keywords

Classification; Linked data

Abstract

Can library classification systems find new ways to deal with charges of bias? Can linked data contribute a more inclusive representation of diverse voices and communities? This panel will discuss the inherent biases present in cataloguing and classification, and consider the potential for linked data to provide a space to highlight and explore the challenging political issues that can arise in our work. These include issues related to jurisdiction, territory, and community with examples drawn from legal classification and the classification of cartographic resources.

Comments

Presented at the TRY Library Conference held May 3, 2016 at the University of St. Michael's College, University of Toronto.

Repository Citation

Borie, Juliya; Knight, F. Tim; and Hale, Jordan, "Challenging Classification Bias with Linked Data" (2016). Librarian Publications & Presentations. 18.
https://digitalcommons.osgoode.yorku.ca/librarians/18

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.