counting keynoter diversity in libraryland

Recently I mentioned to someone that the library speaker circuit is male-dominated, and she was surprised to hear it. It’s certainly a thing that feels overwhelming from the inside — I’ve been part of a 40% female speaker lineup in front of a 90% female audience — but maybe it’s not as much of a thing as I think?

Well. I counted speaker diversity at LITA Forum once; I can count keynote speakers at big library conferences too.

The takeaway: not as bad as I thought gender-wise but still pretty bad for a field that’s 80% female — except, oddly, library technology does better than the average. On the other hand, if you’re looking for non-white keynoters…it’s pretty bad.

In national-scale US/Canadian library conferences…

  • 43% of speakers are female.
  • 74% of speakers are white, 14% black, 7% Asian, 4% Hispanic.

In national-scale US/Canadian library technology conferences…

  • 57% of speakers are female.
  • 71% of speakers are white, 0% black, 21% Asian, 7% Hispanic.(Ouch. I did not want to type that zero.)

A nice surprise

I honestly didn’t expect library tech to do better than the average, gender-wise. This is partly a function of tiny little sample size – only 14 keynoters. But it’s also a reminder that a few people can have a lot of leverage. A big part of what you’re seeing here is that code4lib decided to care: code4lib members went out of their way to nominate female keynoters, and keynoters who can speak to feminist issues, and in the open vote that ensued, the two winners were female. LibTechConf organizers went out of their way to solicit diverse speakers, too. And either of them alone tips the scale to majority female keynoters in libtech.

Thanks, code4lib and LibTechConf. You’re awesome.

Sumana Harihareswara
Sumana gave a killer talk at Code4Lib 2014. This made her one-third of all keynoters of Asian heritage in libraryland last year, and the only Indian-American. Yikes.

Details

I was looking specifically at keynote speakers — the ones who get invited, paid, and put on a stage in front of the full audience. The ones we showcase as representatives of our values and interests; the ones we value most, metaphorically and literally. The ones we ask.

Not everyone uses the term “keynote”; I also counted “opening/closing general session”, “plenary”, and (in the case of ALA Midwinter, which lacks all of those things) “auditorium speaker series”.

I looked at the most recent iteration of the following conferences:

AALL, AASL, Access, ACRL, ALA Annual, ALA Midwinter, ALSC national institute, ASIS&T, code4lib, DLF, LibTechConf, LITA Forum, MLA, OLA Super Conference, OLITA Digital Odyssey, PLA, and SLA. (YALSA’s Symposium doesn’t seem to have keynoters.)

That’s pretty much what I thought of off the top of my head, biased toward libtech since that’s where I have the most awareness. Happy to add more and update accordingly!

Reminder: why I do this

This is what I ask: when you walk into a room, count. Count the women. Count the people of color. Count by race. Look for who isn’t there. Look for class signs: the crooked teeth of childhoods without braces, worn-out shoes, someone else who is counting. Look for the queers, the older people, the overweight. Note them, see them, see yourself looking, see yourself reacting.

This is how we begin.

— Quinn Norton, Count

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2 thoughts on “counting keynoter diversity in libraryland

  1. I had this in mind as I was soliciting ideas for keynote (or as we call them, Vision) speakers from the program committee for NASIG’s 2015 conference. The number of women suggested was zero. I had to dig for those on my own from other sources. As it happens, two of the three vision speakers for the conference will be female, and I’m pretty happy that it worked out that way.

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    1. I’m so glad to hear it’s having some impact.

      And it’s so true about the brainstorming phase, isn’t it? I think if I’m ever leading a conference committee, I may require that the first round of keynote ideas NOT include any X (for whatever demographic values of X seem situationally appropriate). Because the first people who spring to mind WILL be non-diverse usual suspects in many, many cases…even though it is not hard, with a bit of discipline, to think of many equally skilled (or sometimes more skilled!) people who are more diverse. But I think you have to actually do the counts, and require the discipline, or you don’t get there.

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