In January 2019, I took a one-week class over Winter Break about science policy and how America funds innovation. It was a very interesting topic, and overall I quite enjoyed it! One interesting component was that it was interactive: students were regularly encouraged in-class to have discussions and reflect on the material. I thought that was a great idea, though once we began, there was something glaring that I noticed: the male students were talking much more frequently than the female students. I’m sure everyone has anecdotes about this, so I decided to collect a little data: I made tallies in my notebook every time someone of each gender spoke. I certainly wasn’t looking to single anyone out, but I was curious to understand what was going on with a little more clarity.

Soon, I realized that just doing tallies didn’t capture the whole story. There were other noteworthy things that I wanted to try to understand & capture. As is usually the case, we had an unstated norm that you should raise your hand before speaking. But there were some people who tended to break that norm, and especially at first, those people all happened to be men. So I wanted to start also tracking “called upon” participation vs “not called upon” participation.

It felt like some periods of discussion had really gender-diverse back-and-forths while other stretches were male-dominated (and one time female-dominated). Occasionally this felt topic-dependent, such as the higher participation of women in the conversation about Grace Hopper and diverse teams. But more often than not, it was still noticeable during “neutral” topics. It was frustrating seeing some people raise their hand to get called on but then someone else simultaneously spoke anyway (this happened to one woman at least 3 times). I think that when someone got leapfrogged, it had a subtle, exclusionary message of “this is not quite your conversation” (especially when it’d change topics). So I tried generalizing my anonymous tally to an anonymous chain of which gender is speaking (to see if my streak intuition was even happening in this particular class).

The final tally of participation can be seen here. To summarize, men spoke more than twice as often as women in general. Men were four times as likely as women to speak without being called on by the discussion leader. That seemed bad to me.

Data

I’ll start by saying that the data here (and everywhere) is imperfect because counting things like called/uncalled discussion has arbitrary decisions (e.g. should I count the instructor? should I count the discussion leader? Should I count a panelist student that isn’t leader the discussion but spoke up without being called on? How do I count someone speaking up uncalled on during an awkward silence?). Usually I didn’t count the instructor or discussion leader, but sometimes I did if it was an inorganic interruption. If someone responded immediately to someone talking to them, I didn’t count that as a second discussion tally.

With that said, here is the data that I recorded. For Monday, I just had tallies. Around Tuesday afternoon, I started recording a time series of discussion to better try to capture the two bullet points above. When I log a time series, I also show the summary counts here below.

Monday (started counting at 11 am, left class at 3pm):


MaleFemale
Called upon249
Uncalled upon42

Tuesday Morning


MaleFemale
Called upon2721
Uncalled upon102

Tuesday Afternoon


MaleFemale
Called upon4020
Uncalled upon133

Wednesday


MaleFemale
Called upon1311
Uncalled upon51

Thursday


MaleFemale
Called upon2710
Uncalled upon94

Friday


MaleFemale
Called upon86
Uncalled upon11

Discussion

I have the following observations from looking at this data. Underlying all of them, however, is that it should be remembered that this is a rather small sample size. It would be wise to not overgeneralize or read too much into this one experiment. 

  1. Men talked more than twice as often as women, even when called upon. I think there are plenty of plausible explanations for this. In my opinion, regardless of whether one gender actually was “smarter” (whatever that means, if anything), it seemed like men had more confidence / less apprehension to say what they were thinking about.
  2. In the few times I did make a note of the student discussion leader’s gender, I didn’t notice large differences in participation depending on that variable. That doesn’t agree with what my intuition would be, but that’s what my (small) collected sample indicated.
  3. Even when the course staff took great steps (e.g. inviting 50/50 men and women on the Friday panel), gendered norms outside of our class’s control limited how effective these ended up being. For the panel, both women left to care for their sick children, which led to an all-male panel. This was very unfortunate.

Personally, I used to never really pay much attention to gender dynamics in conversations. I didn’t really notice until a close friend pointed out to me: not only are there general trends but that I, specifically, sometimes interrupted/dismissed women during conversation. But he told me this in a non-accusatory way to try to make me feel less defensive, which I think made a world of difference. In my experience, calling someone out for something makes them feel defensive, and it is counterproductive at getting everyone on the same team to address these issues. In reality, we’re all trying our best here, and sometimes there are blind spots we miss. But ever since I noticed that particular spot I’d been overlooking, it’s been a lot harder for me to unsee moments in my own life when women get fewer opportunities to speak. 

Moving Forward

Obviously, I’m just one person. I don’t have many answers for how to make the world a better place. The following are some thoughts that I have about what seem like they would help.

For instructors / discussion leaders

If you’re going to have class discussions (which are a lot of fun & often very helpful), letting things sort themselves out might not lead to a desirable outcome. Obviously, every class is different. But if you do take a hands-off approach, at least notice to see whether that has undesirable effects.

Many of these social dynamics are policed by informal norms of behavior, which differ from person-to-person and culture-to-culture. So it might be helpful to simply establish the rules of the road (whatever you want them to be) upfront, such as by making it clear the discussion leader can step in and say “please wait to be called on before talking”.

Alternatively, you could do a stronger form of participation where students opt-out instead of opt-in. You could go down the row as a “popcorn”, or you could use a system of name tags so that you can cold call people to show the conversation is for everyone (though some people are introverted, so strongly consider giving an opt-out if you’re cold calling people and putting them on the spot).

Also, if many people want to talk, it could be helpful to have a queue (either explicitly on the board or even just informally “Let’s hear from Josh, then Sarah, then Dave”). This can give people a sense of their ideas mattering & other people can’t jump the line.

For everyone

It’s like that old saying goes “You’re not in traffic… you *are* traffic.” In a perfect world, everyone would feel included to say what they want to say without feeling like their contributions are unwanted or unappreciated. Unfortunately, whether we intend it to happen or not, most conversations I’m a part of don’t reach that ideal. I think if everyone in the conversation is mindful about questions like “Am I talking too much?” and “Did that person get to make their point without being interrupted?” then we’ll get a lot closer. 

If you’ve never really noticed or thought of this issue before, you could feel encouraged to do your own informal tally for a week in classes or meetings. It doesn’t need to be capital “R” Research. Maybe you’re in more balanced meetings than I am! If so, please let me know what the secret to success is!

One last note: for this class, I did “contaminate” my data a little bit. After 2 days, I felt like I’d collected enough evidence to be able to point to a real pattern. I figured calling people out would have come across as aggressive & virtue signalling, and would have alienated to people (which could cause them to feel defensive and mistrustful). Instead, I tried talking to my peers one-on-one during class breaks about my informal observations. Some people were more receptive to the concerns than others. I’m not actually sure there is a surefire way to convince someone that this is a problem and that they might also want to be concerned about.  🤷🏻‍♂️

Parting Thoughts

If I may editorialize a bit, it’s always been strange to me when I have conversations with folks about diversity in STEM and they think that biological differences between men and women are likely to explain a lot of these disparities. I’m certainly no biologist, so I can’t definitively comment on the science, but if that were the explanation, how would it explain the history of Computer Science? Comp Sci has many women pioneers, including: Ada Lovelace, Grace Hopper, Katherine Johnson, the ENIAC programmers, and 10,000+ women codebreakers in WW2,. 

In 1985, about 1-in-3 Computer Science students  were women. But the late ‘80s ushered in the era of personal computers, which were marketed for little boys for computer games. Families bought PCs for their sons but not as often for their daughters. Not long afterwards, when those children started in an Intro to CS class, the boys had had a head start on programming experience. Over time, it seems this performance gap solidified into a self-fulfilling prophecy and culture about one’s  “inherent ability” to understand CS. Within a few decades, the women-participation rate of CS was cut in half, even as it rose in other fields such as law, medicine, and the physical sciences. 

Maybe there are some biological differences in some areas – who knows? But for conversations about women in STEM, it seems far more likely to me that we’re dealing predominantly with nurture, not nature.

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