The first peer-reviewed paper from the Queer in STEM survey of lesbian, gay, bisexual, trans, and queer scientists, engineers, and research professionals is now online ahead of print in the Journal of Homosexuality. It’s the first big, nationwide study of LGBTQ career experiences in the sciences — a potentially important resource to inform the policies of scientific employers and professional organizations.
Some of the most important points in the paper, which I wrote with collaborator Allison Mattheis, are
There are a lot of LGBTQ folks working in science, technology, engineering, and mathematics (STEM) — we had more than 1400 responses from STEM professionals across the United States, and in several other countries. (Edited to add: Does this mean LGBTQ folks are well represented, as a proportion of everyone working in STEM? We can’t tell from this dataset — but that’s something we hope to work on in a follow-up study.)
Most survey participants reported being completely open about their LGBTQ identity with their friends and family, but a large subset of them were not open at all with their colleagues or coworkers. (This is similar to the results of a survey of U.S. workers released by the Human Rights Campaign last year.)
Participants were more likely to be open to their colleagues or coworkers if they described their workplace as safe and welcoming.
Participants were more likely to be open to their colleagues or coworkers if they worked in a STEM field with better representation of women (see the figure below). This suggests that in fields with poor gender balance, the climate may be less comfortable for anyone who fails to conform to a straight male gender presentation.
Queer in STEM participants were more likely to be open to colleagues if they worked in STEM fields with better representation of women, as estimated from the U.S. National Science Board’s Science and Engineering Indicators (SEI) report. Regression with all STEM fields (solid line), p = 0.31; with Psychology excluded (dashed line), p = 0.02.
I’m very excited to announce that I’ve accepted a new postdoctoral position as part of the AdapTree project at the University of British Columbia, starting in mid-August. The work I’ll be doing with AdapTree is a dramatic extension of the landscape genomic research I’ve done with Medicago truncatula, studying the genetic basis of adaptation to different environmental conditions. For AdapTree, the focal species are lodgepole pine — Pinus contorta ssp. latifolia — and two species of spruce — Picea glauca, P. engelmanni, and hybrids between them. Using genetic data from thousands of trees at hundreds of sites across British Columbia and Alberta, and growth and performance measurements in big climate-controlled experiments, I’ll get to help figure out what it all means for the future of northern forests.
Apart from the sheer awesomeness of the data, it’s going to be fantastic working with the AdapTree collaborators, which include many biologists whose work I’ve long known and admired: Sally Aitken, Michael Whitlock, Loren Rieseberg, Jason Holliday, Katie Lotterhos, and Sam Yeaman, among others. On top of all that, I get to do it at UBC, one of the premier North American universities for evolutionary ecology, and in Vancouver, one of the most beautiful cities I’ve ever visited. Really, this will be a return to the northern Pacific coast community of biologists where I “grew up” as a graduate student at the University of Idaho, but I’ll be coming back with four years of great experience and learning from my time at Minnesota.
So I was disappointed to read your recent op-ed on the website of The Advocate about the lack of queer role models in science — not because you’re wrong about the problem, but because you missed a big opportunity to start fixing it.
Manhattan, which airs on WGN America and streams on Hulu, follows physicists designing what will become the bomb dropped on Hiroshima, starting about two years before August 6, 1945. The project staff and their families are living in a laboratory campus built and hyper-secured by the U.S. military in the desert near Los Alamos, New Mexico, but in many respects they could be working at any research university today. Here’s my (spoiler-y) list of the parallels, which are sometimes dangerously on-the-nose:
In their new book Making Scientists: Six Principles for Effective College Teaching, (Harvard University Press, $24.95) Light and Micari argue that undergraduate education in the sciences should go beyond imparting a basic set of knowledge, and make learning science more like the experience of doing scientific research.
If teaching science to undergraduates is also a thing you do, may I suggest you go read the whole thing?◼
But so now that it’s all over, how’d it go? Pretty well, on the overall. As much as Citizen Science is meant to be a crash course in scientific reasoning for Bard’s first-year students, it’s also a crash course in teaching for folks like me, who come to the job with experience as teaching assistants, but not in planning or executing a whole course. And judged solely on that level, Citizen Science is amazing.
Let me run through the numbers again: 12 four-and-a-half-hour days with the same 20 first-year students. I spent a fair bit of my Christmas holiday preparing lesson plans, and ended up reworking almost all of that planning in the last three days before class started. From there on, the average workday was something like:
0700-0800h: Wake, shower, breakfast at cafeteria.
0800-0900h: Last-minute lesson prep; classroom set-up, maybe some frantic final copy-making.
0900-1130h: Morning class period. Ideally, no more than one hour of this is PowerPoint presentations and/or videos of TED talks.
1130-1200h: Clean up, collect oneself, wait for the crush of students to move through the cafeteria.
1200-1300h: Lunch at the cafeteria.
1300-1500h: Afternoon class period. Only start this with a video if you want everyone to immediately fall asleep. Class debates are good in this time slot. Assign homework for the next day.
1500-1600h: Clean up, collect oneself, adjust tomorrow’s plans based on what you covered today.
1600-1730h: Exercise. (There’s a respectable campus gym, or nice trails if the weather’s not terrible.)
1730-1900h: Dinner at the cafeteria.
1900-whenever it’s done. Lesson planning and prep; printing and copying of handouts.
2300h: Bedtime, one hopes.
With variations for a four-day rotation in the wet lab and another in the computer lab, plus a “civic engagement” day in which the first-year students go to a local public school to guest-teach science classes for half a day, that’s pretty much the shape of the course. It was exhausting. Boot camp for college teaching. Learning to swim by jumping into the middle of the Hudson River in January.
But that schedule leaves out a multitude of support. First and foremost, Citizen Science faculty have no other personal responsibility than the teaching. Meals are in the campus cafeteria, which provides just fine. Housing is on campus—yes, my dorm room was tiny and ill-equipped, but it was also right around the corner from my classrooms, the communal faculty workspace, the cafeteria, and the gym. So: no cooking, no commute.
Also, it must be said, the Bard student body is pretty great. There were the inevitable exceptions, but most of my class section were smart, friendly, and willing to at least try to tackle any topic I threw at them. Sometimes they were alarmingly informal, and I had to bend a little to accomodate the local concept of punctuality, but if a classroom full of unknown students is a cliff from which a rookie prof dives, these students were also the trampoline at the bottom.
But most importantly, Citizen Science teaching is collaborative. Intensely collaborative. From the moment I arrived on campus, most of my conversations with other faculty members were about lesson plans: what had worked last year, what spurred an amazing class discussion earlier today, what part of the lab procedure left every student confused and irritated. We all started with a six-inch-thick binder of readings, case studies, and worksheets, and then added our own ideas—and swapped, reworked, cut, and rejiggered each other’s ideas.
For me, the flagship example of this was the computer lab. The resource binder had some material on SIR models of disease spread in a population; I wanted to try and teach my students some of the programming language R. So why not build SIR simulations in R?
One faculty member had already developed a nifty interactive model of disease spread in a simulated social network, which included many of the basic concepts necessary to understand more general models, so I started the computer section with that. Next up was an intro-to-R worksheet I’d banged out over the holidays, which covered exactly the programming concepts necessary to code the model, and nothing more. A couple of other faculty members test-drove that worksheet in their own class sections, which had the computer lab earlier in the schedule than mine.
One night’s reading assignment was Anderson and May (1979) [PDF], the original SIR paper; the next day we walked through the math in class. Then I gave my students a worksheet covering some of the graphing capabilities of R, which another of the R-using faculty had developed as followup to my introduction worksheet. And finally, I walked them through the coding necessary to create a simple SIR recursion simulation, complete with a plot of populaiton dynamics over time.
The result wasn’t unqualified success, by a long shot. Some students bogged down in the programming; many glazed over when I started writing equations on the whiteboard. Almost everyone seemed to like drawing graphs in R, though a lot of folks got frustrated by the technicalities of programming syntax even in that context. In the end, most students were able to at least follow me through coding the SIR model, but that was all we had time to do. Given another go-around, I’d provide more structure in the final stretch, with a worksheet that walks through the model coding and how to use the finished model to test specific hypotheses about epidemic dynamics. Also, I’d probably lead with the graph-making, which was more engaging than just pushing variables around on the command line.
But on the whole, I think it worked. My students coded SIR simulations in R, which actually responded to parameter changes the way they were supposed to, and generated pretty graphs in the process. Several students even told me, afterward, that they’ll use R for graphing in the future.
That outcome was really only possible because there were other faculty working on similar ideas, testing things out for me, sharing their own experience and materials. From what I hear, that’s a resource I can’t expect to have when I start teaching my own “real” courses as a full-fledged faculty member. And yet it’s the biggest reason why Citizen Science left me feeling like, actually, I might be able to pull off this whole professor-ing thing after all.◼
Which is to say, it’s about what you’d expect for a Northeastern liberal arts college which has (1) been around awhile, and (2) has money for Frank Gehry. It’s a nice place to walk around in the winter sunshine, after a morning working in this building:
Reem-Kayden Center for Science and Computation. Photo by jby.
Two days after ringing in the New Year, I had to wake up early to catch an eastbound plane. I’m starting out 2013 not by plunging back into the lab-greenhouse-office rotutine, but with a 3-week guest teaching gig at Bard College in upstate New York, as one of the faculty for Bard’s winter-term course Citizen Science.
Citizen Science is part of the Bard freshman seminar, and it’s primarily meant to help bring students up to a basic level of understanding how science and scientific reasoning work. Since the entire freshman class takes it, Bard brings on about two dozen temporary faculty to teach Citizen Science—and, while there are some elements of the course that are in place before we arrive, each faculty member builds his or her own curriculum.
That makes this my very first effort at building and teaching a course from (more or less) scratch. There’s a lot of starting material to work from, provided by the Bard faculty running the program, and by other CS faculty—course development is highly collaborative. But ultimately, what my students do for the next three weeks is entirely up to me—I have to pick readings, plan four and a half hours of in-class acitivites a day, and figure out appropriate homework assignments.
I spent most of my holiday vacation sketching out plans for the course, but I’ve still been scrambling to pull things together in the three days I’ve been at Bard. CS starts on Monday, but there’s an introduction/opening event this afternoon, at which I’ll meet my students and give them their first assignment, Robert Fisher’s essay “Mathematics of a lady tasting tea.” My class roster shows only three science majors out of 20 students—this will be one long exercise in talking about science with educated people who, after this month, may never set foot in a wet lab again.