On page 5, D’Ignazio and Klein write:
Indeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point and begins its own work by asking: How can we use data to remake the world?
One of you may have tried to define intersectionality or intersectional feminism on the previous page! And another one of you may have tried to describe what it means to try to “use data to remake the world”…if so, you are especially well suited to do the work on this page. If not, you still are! Because you can see what your classmates wrote about these terms and aim on the previous page in their comment.
This question is an important one for two reasons:
- It forces us to confront what we mean by data (i.e., how can we know how to remake the world if we don’t know what we mean by data?)
- Right up front, it makes us think about how the work we can do in this class can help make the world more equitable and less oppressive.
We are at the beginning here, so let’s start to think about these two items since we need to think about them first before we can do the work of the course.
What is Data?
On page 10, D’Ignazio and Klein talk more about the word data as rhetorical (again, maybe you or a classmate talked about this! Check out what you/they had to say!).
“Data” holds a special status as information. It is often used as if to say it is “vetted,” “correct,” or “authoritative” information. It is not false, it is true. And so on. But, embedded in that status is an argument. As D’Ignazio and Klein ask, “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 fact and acted upon?”
Data is something argued into existence. There is no “raw data.” It is categorized, organized, analyzed, and all sorts of other “-zeds” before it “is” anything.
Rhetoric has many definitions but one of them is that it can simply mean how people intentionally use symbols to do something. No material in the world is data, it is material. When we do things to that material in order to do any qualitative or quantitative analysis, only then is it data. Rhetoric makes data.
As we talked about last class, the tension of this course will be thinking about the reductiveness of data science and the power of that reductiveness in making knowledge. How you make the “best” sort of reductiveness is thinking carefully about how you argue data into existence.
Important questions:
Does data just “exist”? If something is put in a spreadsheet, it is placed into “boxes” with labels, put in an environment that can have it analyzed, etc. Who made the boxes? How is the analysis conducted and what conditions make this possible? We have to “form” data to fit these possibilities in order to analyze, no? Do we “make” data, then? What does it mean to “make” data and to also “analyze” it (and clean it, and communicate about it)?
No pressure, what do you wanna do?
Before moving on, I want you to start thinking about the kind of topic you might focus on when you work with your own data set (likely a publicly available one that you find or I provide).
There are a lot of topics to explore where data (and by extension, writing) can “remake” the world even if the attempt at that remaking will “necessarily remain unfinished” (D’Ignazio and Klein, p. 5).
We are going to work together in the first few weeks to discover topics you can write about. You will likely find a publicly available data set that is in good enough shape that I can help you work on it (unless you are already well informed enough to do some data science on your own in terms of preparing and analyzing the data).
I have some data sets in mind that you can work on related to things like student loans, income inequality, civil rights, education, public transit, protests, legislative reform, immigration, police misconduct, and more. I find a lot of these data sets through the Data is Plural newsletter. You should subscribe to it, as you might find something that catches your eye that hasn’t caught mine. Numlock News is another good one that can lead you to good publicly available data sets.
Because social justice is a concern of this class, it is a good idea to return to the definition that I have of it that I used on the syllabus. This term is famously broad and ill-defined, and I am using elements from one scholar’s usage that I find useful for the kind of work we will be doing in this class. Here again is that definition:
The term “social justice” can be applied to distribution of material goods (e.g., money, education, housing) and political goods (e.g., recognition, representation), to whom these goods are distributed, and how these goods should be distributed (see Fraser, 2009)
The task, essentially, is to think about how data-driven writing that you produce can make compelling arguments about how material and political goods can be more equitably distributed. Any topic that relates to that aim can work, so let’s get started, together, thinking about what possibilities exist.
Complete the Following Before Moving On
Go to our Discord server and navigate to the category called “Reading Discussion” and find the text channel for 2-3-2021.
Once there, answer the following:
- Why are you taking this class?
- What do you want to see changed in the world and why?
- What is a possible topic you would like to work on and why?
If you notice someone writing about something similar, you can respond to their response instead of making your own. Feel free to extend what they are saying and start to brainstorm together. In the coming weeks, you will be placed in groups for similar topics so it would be great to try to make connections now to see where they might go.
After responding to the three questions (or responding to someone else’s response) in the text channel for 2-3-2021 in the Reading Discussion category of our Discord server, click below to continue on in the Learning Module:
