Home > Introduction > Butch Lab Symposium #2: Butch Stereotypes, Cliches, and Misconceptions

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?

Categories: Introduction
  1. March 4, 2011 at 11:03 am

    This may be one of the most fantastic posts I’ve ever read. Thank you for explaining the binary and the boundaries. And thank you for expanding those boundaries far beyond binary, continuous, or even categorical. Beautiful.

  2. NoJoy
    March 4, 2011 at 11:31 am

    Nice post.

  3. March 4, 2011 at 10:12 pm

    Hi! I’m Wendi. I exist on a statistical boundary, as well.

    Excellent post.

  4. March 5, 2011 at 2:17 pm

    Harrison, this is absolutely beautiful. None of us function well on our boundaries, and each of us is the sum of so many tiny parts (beginning with hexagons!). I love your analogy of the fractal zoom, each Mandelbrot set with its own lacy and complex boundary. Brilliant.

  5. GC
    March 6, 2011 at 8:22 pm

    Awesome post!

  6. Kaitlin
    March 8, 2011 at 11:50 am

    I love the zoom effect you used.

  7. March 28, 2011 at 12:27 pm

    Really fricken cool, Harrison. I love the direction you went with this, I would love to hear more about the ways different fields affect and are affected by gender variance. In computer science, boundary conditions are important in testing how well we’ve done our work. Boundary conditions teach us the most about how we’ve constructed our tidy model of the world. Also, extra geekcool points for using the Mandelbrot set as an example.. brilliant.

  8. April 10, 2011 at 2:55 pm

    Harrison, I’ll be honest and say that much of your post was way beyond my intellectual abilities, but I LOVE the concept, and I’m going to read and re-read until I fully understand it. I really like the idea of zooming in and zooming in and zooming in. It’s perfect. I’ve subscribed so I can read more…

  9. April 10, 2011 at 7:44 pm

    Wow, this is such a unique approach to the question, and it’s fantastic. I think that the statistics-based idea that “no model performs well on its boundaries” really names why mainstream binary-based gender systems are so inaccurate. I’d expand that to say that often boundaries in any system are the most creative and interesting places.

  10. April 11, 2011 at 9:07 am

    I do grantwriting and reporting for a queer organization, and one of the hardest questions we have to answer is that one: “How many men do you serve? How many women?”
    Some/most of our funders – because we are a queer organization – allow for a third category, “transgender.” However, generally speaking, they want the #s to add up to 100% – so you can be a woman, or a transgender, or a man, but not a transgender woman or man.

    We get around it by asking people outright, two ways: one, something like “Our grant reporting often requires we categorize people by gender. If we have to choose one (some funders require it), how should we categorize you?” (with those three options.) We then also have a blank letting people tell us how they identify. It means I end up making assumptions/groupings sometimes, but we are at least able to count people how they want to be counted.

    I have no ideally if this is actually statistically sound. But ESPECIALLY in the context of our mission, it feels like the most ethical choice.

  11. Zev
    April 17, 2011 at 5:49 pm

    thanks for my favorite symposium post so far. what a great perspective and a chilling anecdote about statistics examples.

  12. val1l6
    June 8, 2011 at 2:42 pm

    Most people see me as a femme because my hair reach my lower back. I enjoy having long hair, I like the feel of it. They refuse to see my identity as butch just because of my hair!!! And I refuse to cut my hair to fit a stereotype . I refuse to be what people want / expect me to be. Do I get butch credit because I carry the heavy stuff? Or because I open doors? Or do I lose them because I show my female bits?
    I carry the heavy things because I have good strength in my upper body, that’s all.
    I open doors for people cus my momma thought me that it was a polite thing to do.
    I dress in a more manly way because I enjoy it.
    Despite my clothing my female bits are visible because after all I am a woman, they are there, why hide them?
    I love to work with my hands, it was a special thing between my dad and me, a way for use to bond. Every time I fix something I have that homey-love feeling. And it helps me to clear my mind.
    What about the fact that my work title is “business analyst” is that a factor also?
    According to stereotypes I should lose and win butch point because of all those things.
    Should I check a box every time I do a more masculine or more feminine action/reaction?

    Love your post, had to read it twice to understand it all ;)


  13. Chris
    August 29, 2011 at 11:06 pm

    I just started a Master’s program in Statistics, and I can see that if I continue on this path, the rest of my life will be peppered with M/F data. Ah, well. I’ll just be an outlier who processes the data for the M’s and F’s. Thanks for your post.

  14. November 4, 2011 at 9:21 pm

    Harrison, I miss your voice.

  1. April 9, 2011 at 8:58 am
  2. April 11, 2011 at 7:33 am
  3. April 11, 2011 at 9:09 am
  4. April 11, 2011 at 1:27 pm
  5. April 21, 2011 at 8:57 pm

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