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Our Values and Our Metrics for Achieving Them

Published onOct 30, 2018
Our Values and Our Metrics for Achieving Them

We insist on intersectionality

Feminism has always been multi-vocal and multi-racial, but the movements' diverse voices have not always been valued equally. The women's suffrage movement largely excluded Black women and the abolition of slavery from its agenda. In 1969, lesbian feminists were called "the lavender menace" by straight feminists. But feminism fails altogether if it is only for elite, white, straight, Christian, Anglo women. The work of activists and scholars, particularly Black feminists, over the past forty years insists on a feminism that is intersectional, meaning it looks at issues of social power related not just to gender, but also to race, class, ability, sexuality, immigrant status, and more. It does so, moreover, by looking to collectives as well as individuals, structural issues as well as specific instances of injustice.

We advocate for equity

Equity is both an outcome and a process. Future justice must account for an unjust past in which some groups' knowledges have been valued and others have been "subjugated," as Patricia Hill Collins teaches us. In the process of achieving equity, those of us in positions of relative power must learn to listen deeper and listen differently – with the ultimate goal of taking action against the status quo that benefits us at the expense of others. For this reason, we listen and give priority in the text to voices who speak from marginalized perspectives, whether because of their gender, ability, race, class, colonial status, or other aspects of their identity.

We prioritize proximity

As Kimberly Seals Allers, women's health advocate, says, "Whatever the question, the answer is in the community." People in a community know its problems intimately, and they know which phenomena go uncounted, underreported, or neglected by institutions in power (or, conversely, who is overly surveilled by institutions in power). They also know what interventions will work to solve those problems. In this book, we try to prioritize voices with closer and more direct experience of issues of injustice over those that study a data injustice from a distance.

We acknowledge the humanity of data

We recognize that the transformation of human experience into data often entails a reduction in complexity and context. We further acknowledge that there is a long history of data being “all too often wielded as an instrument of oppression, reinforcing inequality and perpetuating injustice,” as the group Data for Black Lives explains. We keep these inherent constraints in mind as we write, attempting to introduce context and complexity whenever possible, and acknowledge the limits of the methods we discuss as well as their strengths.

We are reflexive, transparent and accountable

Acknowledging that our knowledge is shaped by our own perspectives and limitations, we strive to be reflexive, transparent, and accountable for our work. We are on a journey towards justice and that inevitably involves making mistakes. We are grateful to those who have shown us generosity in letting us learn up to this point. And we respectfully say to our future teachers that you will find in us open listeners – we recognize direct and critical words as a generous offer and a vote of confidence in our ability to hear and be transformed by you.

To that end, we have an evolving table of explicit metrics that will guide us in auditing our citations and the examples that we elevate in the book. We note, here, that our foregrounding of race and racism reflects our location in the United States, where the most entrenched issues of inequality and injustice have racism at their source.

NB: The metrics for this draft (see “Draft Metrics” below) were compiled by Izii Carter, a graduate student of journalism and research assistant for the Data Feminism project. We plan to take these metrics into account as we revise, and will release the final metrics upon the publication of the book.

Structural Problem

Aspirational Metrics to Live Our Values For This Book

Draft Metrics

Final Metrics


  • 75% of citations of feminist scholarship from people of color

  • 75% of examples of feminist data projects discussed led by people of color

Scholarship: 36% from people of color

Projects: 49% led by people of color


  • 75% of all citations and examples from women and nonbinary people

67% of citations and examples from women and nonbinary people


  • Acknowledge that data science, as a field, is premised on economic, educational, and technological privilege

  • 50% of feminist projects discussed come from outside the academy

  • Example or theorist in every chapter that demonstrates how the ideas can be applied without expensive technology and/or formal training

Projects: 88% from outside academy

10 of 10 chapters feature non-academic example and/or theorist


  • 30% of projects discussed come from the Global South

  • Example or theorist in every chapter about indigenous knowledges and/or activism

Projects: 8.5% from the Global South

5 of 10 chapters feature indigenous example and/or theorist


  • Center trans perspectives in discussions of the gender binary

  • Use transinclusive language throughout the book

  • Example or theorist in every chapter from a transgender perspective

3 of 10 chapters feature transgender example and/or theorist


  • Resist assumptions about family structure and gender roles

  • Example or theorist in every chapter that illustrates the power of communal (vs. family) support networks

10 of 10 chapters feature communal example and/or theorist


  • Challenge the dominance of visualization in the presentation of data

  • Example or theorist in every chapter that employs non-visual methods of presenting data

9 of 10 chapters feature non-visual example and/or theorist


  • 50% of feminist projects discussed feature and quote people directly impacted by an issue (versus those who study or report on the phenomena from a distance)

Projects: 49% feature people directly impacted

Nikki Stevens:

I’m concerned that separating structural problems is inherently anti-intersectional and so this data collection about the book is at odds with the values of the book.

Nikki Stevens:

If, as folks studying intersectional data, we cannot practice intersectional data design/collection, there is a fundamental problem with the field.

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