zabouti |||

Still trying to understand the word intersectionality”

The introduction to the MIT Press book Data Feminism tries to define intersectionality.

Key to the idea of intersectionality is that it
does not only describe the intersecting aspects of
any particular person’s identity (or
positionalities, as they are sometimes termed).
It also describes the intersecting forces of
privilege and oppression at work in a given
society. Oppression involves the systematic
mistreatment of certain groups of people by other
groups. It happens when power is not distributed
equally—when one group controls the institutions
of law, education, and culture, and uses its power
to systematically exclude other groups while
giving its own group unfair advantages (or simply
maintaining the status quo). In the case of
gender oppression, we can point to the sexism,
cissexism, and patriarchy that is evident in
everything from political representation to the
wage gap to who speaks more often (or more loudly)
in a meeting. In the case of racial oppression,
this takes the form of racism and white supremacy.
Other forms of oppression include ableism,
colonialism, and classism. Each has its particular
history and manifests differently in different
cultures and contexts, but all involve a dominant
group that accrues power and privilege at the
expense of others. Moreover, these forces of power
and privilege on the one hand and oppression on
the other mesh together in ways that multiply
their effects.

The book discusses …

...what we call data feminism: a way of thinking
about data, both their uses and their limits, that
is informed by direct experience, by a commitment
to action, and by intersectional feminist thought.
The starting point for data feminism is
something that goes mostly unacknowledged in data
science: power is not distributed equally in the
world. Those who wield power are
disproportionately elite, straight, white,
able-bodied, cisgender men from the Global
North. The work of data feminism is first to
tune into how standard practices in data science
serve to reinforce these existing inequalities and
second to use data science to challenge and change
the distribution of power.

What counts as data’?

We both strongly believe that data can do good in the world. But for it to do so, we must explicitly acknowledge that a key way that power and privilege operate in the world today has to do with the word data itself. The word dates to the mid-seventeenth century, when it was introduced to supplement existing terms such as evidence and fact. Identifying information as data, rather than as either of those other two terms, served a rhetorical purpose. It converted otherwise debatable information into the solid basis for subsequent claims. But what information needs to become data before it can be trusted? Or, more precisely, whose information needs to become data before it can be considered as fact and acted upon? Data feminism must answer these questions, too.

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