Heads up: I clipped the above image from the Data Feminism website. It’s not mine.

My buddy Yakov Bressler has been the source of many books recommendations that have greatly expanded and clarified how I think about systemic issues in our society and what the role of evaluation is in supporting progressive changes. Far and away, his #1 recommendation was Data Feminism, by Catherine D’Ignazio and Lauren Klein. This book frames really well how understanding community context requires a thorough examination of the factors that lead to so-called unambiguous data.

To structure the book, the authors lay out several principles, which I quote below directly below:

1. Examine power. Data feminism begins by analyzing how power operates in the world.
2. Challenge power. Data feminism commits to challenging unequal power structures and working toward justice.
3. Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from.
4. Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
5. Embrace pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
6. Consider 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.
7. Make labor visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.

For those of us that work in participatory setting, these don’t come as a surprise per se. I know that I have to do a better job of considering underlying power structures that yield the data.

This also ties into one of my side research areas, which is finding unobtrusive measures that can inform us about context. This seems all good, but could get us into a few traps. The first, as the authors point out in Chapter 6, is that big data can be stripped of contextual factors that tell us important ground-level characteristics. The second is that it strips the power of individuals to tell their own stories. So there needs to be a balance that both acknowledges evaluation burden while also providing space for reflection of the data.

I can honestly say I have not yet solved this problem. I’ve staring to track twitter trends of certain hashtags, but this only tells us about the people who are posting on twitter. While specific organizations may have a good online presence, it doesn’t exactly provide in depth information about the specific implementation and implementation challenges of a project. But that was a digression.

This book is available online at the button below, and I strongly encourage you check it out.