The Feminist Pedagogy Project

Author: Klein, L. F.

The Numbers Don’t Speak for Themselves

Author(s):	D’Ignazio, C. Klein, L. F.
Date:	2020
Publication:	Data Feminism
Citation:	D’Ignazio, C. & Klein, L. F. (2020). The Numbers Don’t Speak for Themselves. In Data Feminism (149-172). MIT Press. https://doi.org/10.7551/mitpress/11805.003.0008

Section on webpage:	Critical Data Justice Literature
Tenets:	Using technology intentionally to build communities and enhance learning.
Annotation:	In this chapter of Data Feminism, D’Ignazio and Klein introduce the principle of considering context, and walk through situating data on the web, viewing data as partisan, communicating context, and restoring context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis. The authors begin the chapter with an error made by media sources referencing the Global Database of Events, Language and Tone (GDELT), a database that, like many others, is characterized by a totalizing and dominating framework as enacted through data capture and analysis. They state that the contextualization of data is just as important as its availability, and provide the United States’s and Brazil’s apparent data transparency as examples; although the data is in theory available to the public, a lack of metadata and understanding of the government systems from which the data originate make it practically inaccessible to possible users. In this light, the authors advocate for a viewing of all data as “cooked” – that is, already a product of numerous social relations and data sorting methods.

February 11, 2026

The Numbers Don’t Speak for Themselves

Author(s):	D’Ignazio, C. Klein, L. F.
Date:	2020
Publication:	Data Feminism
Citation:	D’Ignazio, C. & Klein, L. F. (2020). The Numbers Don’t Speak for Themselves. In Data Feminism (149-172). MIT Press. https://doi.org/10.7551/mitpress/11805.003.0008

Section on webpage:	Critical Data Justice Literature
Tenets:	Using technology intentionally to build communities and enhance learning.
Annotation:	In this chapter of Data Feminism, D’Ignazio and Klein introduce the principle of considering context, and walk through situating data on the web, viewing data as partisan, communicating context, and restoring context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis. The authors begin the chapter with an error made by media sources referencing the Global Database of Events, Language and Tone (GDELT), a database that, like many others, is characterized by a totalizing and dominating framework as enacted through data capture and analysis. They state that the contextualization of data is just as important as its availability, and provide the United States’s and Brazil’s apparent data transparency as examples; although the data is in theory available to the public, a lack of metadata and understanding of the government systems from which the data originate make it practically inaccessible to possible users. In this light, the authors advocate for a viewing of all data as “cooked” – that is, already a product of numerous social relations and data sorting methods.