I am also always trying to puzzle through the “master’s tools” problem. Each chapter in Data Feminism is rich with examples; I’ll try to bring a few in from the other chapters on Wednesday to see if they help us think through these problems.

One that comes to mind, though, is Mimi Onuoha’s Library of Missing Datasets…[Read more]

I agree, let’s talk about some of these binaries this week!

Thanks for these thoughts, Rabani. This is a great distillation of Gitelman and Jackson’s intro in particular, in the context of our big picture questions for this week. You’re also making me think about the term “raw data” in the context of the discussion of “clean” vs “messy” data in some of the other readings. Something that is “raw” (not…[Read more]

These are great questions, and I’d like to take up some of them in class so we don’t get bogged down with threads of text. But quickly, on data and structure, I think the discussion of cataloguing in “Against Cleaning” is particularly helpful. Structures like MARC records or Library of Congress Subject Headings exist to help delimit fields of…[Read more]

I appreciate you calling our attention to “subversion,” Jeanna. I like Max’s interpretation, that subversion is actually tactical across all 3 domains (since subverting heteronormative patriarchy is built into feminist practice).

I’m wondering if we can think of some projects that do this work, as a way of grounding the discussion. For…[Read more]

One way to think about this issue is to go back to our discussions about public vs private interests. Google is a for-profit company, but it’s also become the default way we access information — and it’s in the public good for that default way to be unbiased. (Think about public libraries; we wouldn’t want anyone going into a library collectively…[Read more]

Understanding our biases *and* that we can’t get over them will have to be key principles for our project design. Thanks for sharing — and never apologize for doing so!

Great points, Junyoung! And you’re right about Drucker not being a neo-Luddite. Let’s talk about this more in class.

You’ve identified a really big problem in the field, Eric. Say you’re a scholar or student in the humanities, and you have 20 work hours to do your research. Then say you need 12 hours just to learn the coding/tools/skills necessary to build a corpus, clean up the data, and process it. That leaves you with only 8 hours to actually interpret and…[Read more]