Butch Lab Symposium #2: Butch Stereotypes, Cliches, and Misconceptions
It seems silly that I didn’t participate in Butch Lab’s Symposium #1, considering the content of this blog. I promised myself that I would participate in Symposium #2…and I am! Just late.
The prompt is:
What do people think “butch” means? What are the stereotypes around being butch? What do people assume is true about you [or the masculine of center folks in your life], but actually isn’t? What image or concept do you constantly have to correct or fight against? How do you feel about these misconceptions? How do you deal with them? Do you respond to these stereotypes or cliches? How?
Isn’t that the whole point of this blog?
I’m probably going to take this in a different direction that the author of this prompt originally intended. My academic background is in math: specifically, probability, and a growing knowledge base in statistical theory. In undergrad classes, one of the things that really bugged me was something I recently saw listed on the Microaggressions tumblr:
In my statistics class this morning in a discussion of basic polls and data-gathering, my professor uses gender as the example and explains that the two categories are male and female, and you have to be in one and that they are mutually exclusive.
I am a genderqueer/genderfluid person. This made me feel invisible, like a lie, irate and like I need to leave.
Gender is pretty much THE example of a binary variable in introduction to statistics classes. I can’t tell you how many times I sat through an explanation of a binary variable only to hear, “The categories are male and female: each person belongs to one, and one alone.” And every time, it really really hurt. But it doesn’t have to. Consider that there are different types of variables. Just a few are:
- Binary. Example usually given: gender.
- Continuous. Example usually given: height.
- Categorical. Example usually given: race.
Even funnier is that every statistics professor in the world will tell you that the way that the measurement of the latter two variables, continuous and categorical, is open to interpretation. How are you defining “race”? Are you making people select from a list or using write-in answers? How are you measuring height? Are you wearing shoes? Is your scale accurate? They emphasize the proper collection of these attributes because they seem so much more open to error or bias. But gender? Most of them don’t think of it as something measurable, something that requires interpretation.
We, readers of gender blogs, already know that gender does require interpretation. How are you measuring it? Self-reporting? Survey collector’s impression? How are you accounting for error or bias? The truth is that gender alone could be its very own statistical model. To us, it is vastly complex. Why is that? I’d argue it’s because of something that a professor once said in lecture:
No model performs well on its boundaries.
I wrote that down in the margin of my notebook. It wasn’t important to the class but it was hugely important to me. Think of gender as a binary model – something most human beings plug coefficients into to get either a 0 (traditionally male) or a 1 (traditionally female). Imagine that the model is this (it isn’t):
Genital Configuration + Dress + Hair Length + Speaking Voice + Interests + Sexuality = Gender
Imagine that all of the variables on the right side sum to 1. Then, in order to get either a 0 or 1, ALL of the coefficients attached to each variable must be either 0 or 1. That’s why when one of these things doesn’t conform to the expected output, people get confused.
But all of us queers exist on the boundary of the traditional gender binary model. We’ve already challenged the output by not having the expected sexuality, at the very least. So we’ve invented a new model, one that accounts for the gender diversity that we see reflected in our worlds. It’s more complex than a binary variable. That’s where we get the rest of our words: “butch”, “transgender”, “queer”, “genderqueer”, “femme”. But is it complex enough? Let me ask another question: have you ever seen a Mandelbrot zoom?
Every time you think you know the structure of what you’re looking at, you get closer and realize it’s infinitely more complex than you could possibly have imagined. That’s why, under critical self-examination, even our own genders, which we “should know” and “are stable”, shift more than we anticipated.
Of course, that’s no way to go through life, all the time. You need the power that these statistical models give you, otherwise you’d be consistently overwhelmed by the amount of information in your life.
Butch stereotypes exist, but it depends which statistical model you’re using. If you’re using the model that most American society uses, here are some of those stereotypes: We’re quiet. We’re unattractive. We hit on straight girls. We’re fat. We have bad haircuts. We’re all lesbians. We’re all women who want to be men. Now zoom in, to what might be a mainstream LGBT perspective: We’re anachronistic. We disrespect women. We buy into a patriarchal culture that privileges masculine behaviors over female ones. We hate our bodies. We shouldn’t be the face of the movement. Zoom in. We might hate our bodies, but it might be because we’re trans. Or we’re not. We all have short hair. We wear mostly men’s clothes. Some butches like to date other butches. Some butches like to date men. We’re kind of incredibly hot. Zoom in. Hi, I’m Harrison. I exist on a statistical boundary. How about you?