Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets

Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins...

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Autores principales:	Nina Exner, Erin Carrillo, Sam A. Leif
Formato:	article
Lenguaje:	EN
Publicado:	University of Massachusetts Medical School, Lamar Soutter Library 2021
Materias:	antiracism data consultations data collection quantcrit racial demographics social justice race classification data categories Bibliography. Library science. Information resources Z
Acceso en línea:	https://doaj.org/article/476c6f3ced6d40e888f6e2d27578296b
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spelling	oai:doaj.org-article:476c6f3ced6d40e888f6e2d27578296b2021-11-18T17:42:56ZData Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets10.7191/jeslib.2021.12132161-3974https://doaj.org/article/476c6f3ced6d40e888f6e2d27578296b2021-11-01T00:00:00Zhttps://escholarship.umassmed.edu/jeslib/vol10/iss4/4/https://doaj.org/toc/2161-3974Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Nevertheless, the Census categories explicitly say that they have no basis in research or science. Therefore, social justice requires that data librarians should expose researchers to this fact. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories. Conclusions: We suggest that many studies are harmed by including race and should remove it entirely. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection.Nina ExnerErin CarrilloSam A. LeifUniversity of Massachusetts Medical School, Lamar Soutter Libraryarticleantiracismdata consultationsdata collectionquantcritracial demographicssocial justicerace classificationdata categoriesBibliography. Library science. Information resourcesZENJournal of eScience Librarianship, Vol 10, Iss 4, p 1213 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	antiracism data consultations data collection quantcrit racial demographics social justice race classification data categories Bibliography. Library science. Information resources Z
spellingShingle	antiracism data consultations data collection quantcrit racial demographics social justice race classification data categories Bibliography. Library science. Information resources Z Nina Exner Erin Carrillo Sam A. Leif Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
description	Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Nevertheless, the Census categories explicitly say that they have no basis in research or science. Therefore, social justice requires that data librarians should expose researchers to this fact. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories. Conclusions: We suggest that many studies are harmed by including race and should remove it entirely. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection.
format	article
author	Nina Exner Erin Carrillo Sam A. Leif
author_facet	Nina Exner Erin Carrillo Sam A. Leif
author_sort	Nina Exner
title	Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
title_short	Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
title_full	Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
title_fullStr	Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
title_full_unstemmed	Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets
title_sort	data consultations, racism, and critiquing colonialism in demographic datasheets
publisher	University of Massachusetts Medical School, Lamar Soutter Library
publishDate	2021
url	https://doaj.org/article/476c6f3ced6d40e888f6e2d27578296b
work_keys_str_mv	AT ninaexner dataconsultationsracismandcritiquingcolonialismindemographicdatasheets AT erincarrillo dataconsultationsracismandcritiquingcolonialismindemographicdatasheets AT samaleif dataconsultationsracismandcritiquingcolonialismindemographicdatasheets
_version_	1718420722301272064

Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets

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