data feminism chapter 1: the power chapter

Question 1

Key Takeaways

Answer 1

Serena Williams’ story shows how data gaps and systemic bias affect Black women’s healthcare.

Question 1

Data Feminism — Chapter 2: Collect, Analyze, Imagine, Teach

Case Study: DGEI Map (1971)

Answer 1

Mapped locations where Black children were killed by white commuters in Detroit.

Youth-led project showed how to collect counterdata in the absence of official records.

Question 2

Redlining Map Comparison

Answer 2

Government-created maps labeled Black neighborhoods as high-risk, reinforcing systemic inequality.

Demonstrates how data tools can enforce oppression if misused.

Question 3

Four Tactics for Challenging Power

Answer 3

Collect: Make new data when it’s missing (counterdata).

Analyze: Expose inequity using stats or visual tools.

Imagine: Dream alternative futures—not just fairness, but co-liberation.

Teach: Change who gets to work with data.

Question 4

Data Feminism — Chapter 3: Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints

Key Example: Periscopic’s Gun Death Visualization

Answer 4

A visual display of stolen years from gun violence.

Used emotion and narrative to provoke empathy—unlike traditional charts.

Question 5

Main Argument

Answer 5

Emotion ≠ bias.

Visualizations are never neutral—even spreadsheets carry bias.

Question 6

Donna Haraway’s “god trick”:

Answer 6

The illusion of seeing everything from nowhere. Neutrality is a myth.

Question 7

Feminist objectivity =

Answer 7

acknowledging partial perspectives and incorporating emotion and lived experience.

Question 8

visualization is rhetorical:

Answer 8

it persuades and reflects choices, even when it claims neutrality.