Thread by @albanvillamil

@laurenfklein: In today's world, data is power. Data science needs intersectional feminism, which provides models to examine power, to challenge the matrix of domination that continues to oppress certain groups and not others #DataFeminism at @datasociety

mitpress.mit.edu/books/data-fem…

@kanarinka: For example, María Salguero's #NiUnaMenos project amassed the largest public database for femicides in Mexico after the Mexican government refused to track these deaths. This is a form of feminist counter data.

#DataFeminism at @datasociety

7 Principles of #DataFeminism:
1) Examine power
2) Challenge power
3) Rethink binaries and hierarchies
4) Elevate emotion and embodiment
5) Embrace pluralism
6) Consider context
7) Make labor visible

#databites at @datasociety

With #DataFeminism, context matters.
•Who is doing the work?
•Who is the data about?
•Who is being harmed? Who is benefiting?

But also consider context over time. What if there is an administration change? When do you want to be counted?

#databites at @datasociety