The Pip was sick this weekend, I had a deadline for a bunch of administrative crap that I pushed off back in December when I was rushing to finish the book, and I’m giving an exam on Thursday. So, I’m not doing lengthy blogging right now, but two quick notices:
2) Rhett Allain and I will be doing another G+ hangout, this time about our experiences blogging in academia. We’re announcing this one in advance in case anybody would like to watch it live. I think there’s also an opportunity to participate via chat, which might be useful as Rhett and I might be too much in agreement about some things, and we could probably use some input from a different POV. We haven’t done this before, though, so I’m not 100% sure how this works. It’ll be an adventure.
My trip into the office today was for the express purpose of posting this job ad:
We invite applications for Visiting Assistant Professor starting in September 2014. This position is available for up to three years, contingent on satisfactory performance. Applicants should have some teaching experience and a strong commitment to undergraduate education. Union is a highly selective, small (2200 students) liberal arts college with an engineering program, located in the Capital District of New York State, a region heavily engaged in science and R&D. The Department of Physics and Astronomy (www.physics.union.edu) includes eleven full-time faculty in a variety of fields including astronomy, astrophysics, atomic physics, biophysics, environmental physics, nuclear physics, materials science, statistical mechanics, and art restoration research. Union College is an equal opportunity employer and is strongly committed to increasing the diversity of its workforce. Applicants should send a detailed curriculum vita, statements of teaching and research, and arrange for three letters of reference to be sent to [address removed to deter spammers– follow the link above for information]. We will begin the review of applications on March 1, 2014 and continue until the position is filled.
So, if you’ve ever thought you’d really like to work here, now’s your chance. Click on that link, and submit an application.
Finally, after 15 or so years of parents managing every variable, there comes the time when a student is expected to do something all by herself: fill out the actual application. Write an essay in her own voice.
By this point, our coddled child has no faith in her own words at all. Her own ideas and feelings, like a language she has not practiced, have fallen away.
Her parents wanted more than anything to protect her, to give her the world. Instead they’ve taken away her capacity to know it.
Faced with that blank page, the students panic. They freeze. Their entire lives have been pointed toward this one test of their worth: Who wouldn’t suffer writer’s block? The parents yell. Everyone sobs.
The reason for the widespread use of oversimplified measures is that they’ve become necessary. They stink, all right, but they’re the smallest evil among the options we presently have. They’re the least stinky option.
The world has changed and the scientific community with it. Two decades ago you’d apply for jobs by carrying letters to the post office, grateful for the sponge so wouldn’t have to lick all these stamps. Today you apply by uploading application documents within seconds all over the globe and I’m not sure they still sell lickable stamps. This, together with increasing mobility and connectivity, has greatly inflated the number of places researchers apply to. And with that, the number of applications every place gets has skyrocketed.
Simplified measures are being used because it has become impossible to actually do the careful, individual assessment that everybody agrees would be optimal. And that has lead me to think that instead of outright rejecting the idea of scientific measures, we have to accept them and improve them and make them useful to our needs, not to that of bean counters.
On one level, these are completely different sorts of stories, but in thinking about the two in close succession, it occurred to me that in some way, they’re distorted reflections of each other. In both cases, a big part of the problem is, as Bee identifies, the vast oversupply of applicants relative to the number of open spots. The “common application” for colleges has had a similar effect to the ability to email job applications– application numbers are way up at every college and university. Relatively crude measures like test scores thus end up playing a larger role in admissions than might be ideal, just in order to get through the huge pool of applicants.
But it’s even worse than that. An admissions officer once remarked that they get dozens of applications from students who probably have no real interest in Union, but throw an application our way anyway because it’s just another click of a checkbox on the common application form. The problem the folks in admissions face is not just identifying the best students in the applicant pool, but the best students who are likely to actually come here.
That, in turn, is a problem driven by the use of oversimplified metrics– the infamous US News rankings include the fraction of students admitted as part of their calculations, and the lower you can make that, the better. Offering admission to outstanding students who are overwhelmingly likely to turn you down actually hurts your ranking compared to offering admission to less outstanding students who are overwhelmingly likely to accept. (You occasionally hear stories of outstanding students who get rejected by their “safety schools” for this reason– they were too good to plausibly want to attend, and thus denied admission to keep the selectivity numbers up. These may be apocryphal, though…) Which is part of why there are so many people reading files and letters so closely, trying to parse the essays crafted under the tutelage of professional consultants to find the students who are actually both good and interested.
On the faculty side, meanwhile, Bee points out that there are ways to game the system of crude metrics that we have, and that having faculty deliberately targeting these metrics further distorts the whole process. And it occurred to me that a lot of this system-gaming, and even the advice given to faculty candidates, is really the moral equivalent of hiring professional application essay consultants. Padding out a CV with “minimum publishable units,” or specifically targeting certain types of publications, like getting paid advice on how to craft a compelling college essay, is a way of buffing up an otherwise lackluster application in an attempt to get past relatively crude filters.
(I will admit a little bafflement at the idea of using single-author publications as a measure of success– I come out of a field where single-author publications just do not happen. I can think of one remotely significant single-author experimental paper in the last twenty-odd years of AMO physics. But then, this measure is attributed to Lee Smolin, who’s pretty bad about mistaking the tiny subfield of theoretical high-energy physics for the totality of physics, so…)
So, when it comes to crude metrics and trying to game them, I suspect we have more in common with our students than we might think. And, like Bee, I agree that we need new and better measures at all levels of academia– standards that are simple enough to apply very broadly and efficiently, but that nevertheless are accurate enough to capture what we’re really looking for. I especially want somebody else to develop these, because I haven’t the foggiest idea how to go about it…
(While I’m mentioning the NYP thing, though, I’ll note one aspect of the story that was a little disappointing to me– I would’ve liked to know more about the student who framed the story. I have some sympathy for her plight as a general matter– my college essay writing went through a bunch of iterations, and the final version came only after I trashed my parents’ basement in frustration– but the final essay sounds pretty interesting. I’m curious, though, whether the author thinks it actually reflected the student’s real potential, justifying her admission, or if her acceptance was just the inevitably result of having her surname already on a campus building, and the essay they came up with merely passed the minimum bar needed to justify that.)
I say “amused” because of the coincidence in methods, not because of the content. And, in fact, the content is… not likely to make them friends in a certain quarter of the blogosphere. I actually flinched when I read the sentence “This is further evidence that there is no systematic bias against hiring women.” That phrasing is incredibly unfortunate in that it is likely to be interpreted in ways that will really upset some people.
It is, however, a true statement given their analysis and the relatively narrow question they’re trying to answer. And it’s an important enough point that it’s worth writing up in a little more detail than IHE or ZapperZ did, to make clear what they are and are not saying. I’m not going to do this is funny Q&A format, though– writing about this at all is somewhat fraught, and trying to crack wise while writing about this study is basically guaranteed to blow up in my face, so I will keep this as matter-of-fact as I can.
The new study is a statistical analysis of the distribution of women in physics, attempting to address the question of all-male departments. The statistics are pretty striking, and summarized in this table that I cribbed from Inside Higher Ed (hopefully the formatting won’t go all wacky due to differences in CSS):
Physics Departments, Faculty Size and Gender
Highest physics degree awarded
Smallest department (# of faculty members)
Median size of department (# of faculty members)
Largest department (# of faculty members)
Women's representation on physics faculty
Departments that have no women
Departments that have no men
Number of departments
Those data seem pretty damning at the bachelor’s-only level: nearly half of all physics departments have no women at all. Surely, this is evidence of bias, right? Those all-male departments must be a result of old-boy networks of sexists who won’t hire women.
What the study shows, however, is that this can’t actually be taken as evidence of bias, because many of these departments are very small– the median size of a department at a bachelor’s-only institution is four professors (meaning, for the record, that Union, where I work, is way above average– we have eight tenure lines and two (soon to be three) permanent but non-tenured lecturer positions). Given that, there’s actually a pretty decent chance of ending up with an all-male department just from basic statistics.
They demonstrate this by basically the same method I used in the first of the baseball posts linked above: they set up a simulation where they take an imaginary population of faculty with the same gender ratio as in the real sample, and assign them to departments of the same sizes found in the real sample completely at random. This is a little more complicated than the simple toy model I used for the baseball thing, because the probability of getting each gender changes as they assign faculty to departments. Then they look at what fraction of the imaginary departments ended up being all male. They repeated this 500 times, and got the graph showing how many of their 500 simulations gave a particular percentage of all-male departments that’s the featured image up top, which I’ll repeat here:
What you see is something that looks pretty much like a classic “bell curve” distribution, showing that there’s an average fraction of departments with no women, and some uncertainty about that average. The yellow bar indicates what they see in the actual sample, which you can see is slightly below the peak of the distribution (the most likely value is 49%, they see 47%), though within one standard deviation of it. The same graph for Ph.D.-granting institutions looks like this:
Again, you see an average with some uncertainty, but the yellow bar is a little harder to see, because it’s way off in the left wing. The observed number of all-male departments is much smaller than you would expect from a random distribution– the most likely value is 12%, and the real sample is 8%. In fact, 90% of their simulations gave higher all-male fractions (so the real value is a bit less than two standard deviations below the mean). The overall numbers are all much lower, reflecting the larger average size of departments at Ph.D. granting institutions– given a larger number of faculty, the odds of a random draw ending up all-male are much lower.
This suggests that if there’s any systematic preference happening in hiring, it goes in the opposite direction of the most basic sort of sexism– women are, in fact, somewhat more broadly distributed among departments than you would expect from simple chance. The fact that a sizable fraction of physics departments do not have any women is not by itself an indicator of bias against women, given the fraction of the faculty pool who are women. If you wanted to insist on putting a negative spin on this, you could try to argue that it’s indicative of some sort of tokenism– a willingness to hire one woman so the department isn’t entirely male, but not more than one. But that doesn’t seem all that likely, and if anything goes a bit against the conventional wisdom about women on hiring committees and so on.
Now, there’s a big and important caveat to this, which is the emphasized clause in the previous paragraph. They have assumed a particular gender distribution among the imaginary faculty pool in their simulation, which matches the gender distribution of the real sample– 16% of the bachelor’s-only faculty are female and 11% of the Ph.D.-granting pool. That’s descriptive, not prescriptive– they would undoubtedly prefer a more equal split (and in fact re-run their simulations for higher fractions of women), but they’re looking at what’s actually out there for the purposes of this analysis. And it’s important to remember that those low percentages are over all ranks of faculty– from newly hired assistant professors to the moldiest of why-won’t-he-retire-dammit full professors– and thus include the effects of decades of past hiring decisions.
(They do, for what it’s worth, give one quick indication of the present state, in their Figure 5, which shows that the percentage of women increases as you move to younger cohorts. The Assistant Professor (that is, pre-tenure) ranks are over 20% female, a percentage that’s slightly higher than the fraction of women receiving Ph.D.’s in recent years. This is where you’ll find the sentence quoted above that made me flinch– “This is further evidence that there is no systematic bias against hiring women.” Which is, as I said, a somewhat unfortunate phrasing, but not an inaccurate summation of their data: women are hired into tenure-track faculty positions in the same proportion that they graduate with Ph.D.’s, so looking at the system as a whole from the 30,000-foot-altitude kind of level, there’s no clear indication of bias– if anything, there’s a very slight preference for hiring women. Though they note that even with a higher fraction of women in the pool, you would still expect some number of single-sex departments– they ran simulations with numbers matching the assistant professor distribution (where, again, the proportion of women is slightly higher than among recent Ph.D. graduates), and those would give 37% single-sex departments at the bachelor’s level and 3% at the Ph.D. level. Even at a 50-50 split among faculty, you’d end up with around 10% of bachelor’s-only departments having no women (though in that case you’d also get 10% with no men…).)
What are the limitations of this? Well, it’s a very global, 30,000-foot-altitude kind of study. All they can really say is that, on the basis of statistics, it is unlikely that there is a global, systematic bias against women that leads to the large number of all-male departments. The fact that a department has no women, particularly at a smaller school, does not necessarily indicate any bias in hiring beyond whatever may be indicated by the gender distribution of the available faculty pool.
This does not mean that there is absolutely no sexism anywhere, and that’s not what they claim. All they can and do say is that the distribution we see in reality is no worse than you would expect from a purely random distribution. This does not rule out the possibility of bias in any individual department, or even some large number of biased departments, provided they are balanced by some number of unbiased or oppositely biased departments.
This also doesn’t say anything about what kinds of jobs people have, or what they’re paid, or any of a host of other kinds of potential bias. You could undoubtedly construct a pathological sort of system whereby the global appearance of no bias was produced by systematically excluding women from a small number of highly prestigious positions while distributing them more evenly among a larger number of low-status jobs. This kind of analysis will not allow you to detect that sort of problem (though there are other ways to get at those kinds of questions, and I have no doubt that AIP and other organizations are doing those tests).
This is basically analogous to the situation in the second of those baseball posts linked at the beginning. The batting average data I was playing around with are broadly consistent with what you would expect for a single, constant “innate” average– that doesn’t mean that you can’t have an innate average that changes with time, just that you can’t point to anything in the statistics that unambiguously shows those sorts of changes. Similarly, these data are broadly consistent with an unbiased (in a statistical sense) random distribution of women among faculty jobs, and that doesn’t mean bias doesn’t exist, just that there’s nothing in the statistics that unambiguously indicates the presence of bias.
(Now, there are other arguments you could raise about this, like whether we ought to expect or want the hiring of candidates for faculty positions to resemble a random distribution. You could also argue that there ought to be a much stronger preference given to women in order to equalize the overall distribution, but that’s a flamewar of a different color. And, obviously, a much more equal distribution in the candidate pool (more that 20% women) would be a wonderful thing, though that’s an issue with an earlier part of the pipeline than they’re considering here. Again, this study is descriptive, not prescriptive.)
Every now and then, I run across a couple of items that tie together a whole bunch of different issues that weigh heavily on my mind. That happened yesterday courtesy of Timothy Burke, whose blog post about an NPR story is so good that there aren’t enough +1 buttons on the entire Internet for it.
The NPR piece is about eating and exercise habits, and the way families struggle to do what they know they ought to:
More than half of children ate or drank something during the “crunch time” window that can lead to unhealthy weight gain, as perceived by their parents. And more than a quarter of children did not get enough exercise, their parents say.
“It’s hard enough to get dinner on the table while trying to help them with homework,” says Paige Pavlik of Raleigh, N.C. “Once we do everything, there is absolutely no time to go outside and take a walk or get any exercise. It’s simply come in, eat, sit down, do homework, go to bed.”
The relentlessness of it makes her emotional. Pavlik starts to cry as she talked about her family’s daily crunch time. “It’s really hard,” she says. “This isn’t how I thought family life was going to be.”
It was kind of hard to read the rest of that, from my head nodding so much. We deal with exactly this sort of issue every day in Chateau Steelypips, and there are days when I don’t think I can take it any more.
By the time both kids are up, fed, dressed, and ready to go, it’s basically 9:00. Pack them off to day care, take Emmy for her walk, and get my own stuff together, and it’s generally 10 before I’m in the office. I spend the morning grading, getting my afternoon class together, and dealing with whatever GIANT EMERGENCY has sprung up that week, then teach my class. My class ends at 3, and on a good day I get to go home about 4:30 to get dinner together (I do basically all of the shopping and cooking). On a bad day, it’s 5:30, and I don’t go home first, but straight to day care to get the kids. Kate gets home at 6, if we’re lucky we have dinner before 6:30, and after fighting for half an hour to get SteelyKid to eat, I’ve about got time to respond to email and walk the dog again before SteelyKid’s 8:00 bedtime. Which takes the better part of an hour, then there’s housework to deal with, and usually by 9:30 or 10:00 I’m free to work on whatever I have going on. Or faceplant into my keyboard, whichever is easier.
And believe me, I’m well aware that we have it good– neither of us is punching a clock at an hourly wage job. If I want to duck out a little early, I can, provided stuff gets done. Kate’s a little more constrained, but not all that much. Nobody’s going to fire either of us for taking a little time here and there to deal with family matters. And we’re well-off enough that if we really needed to, we could fix some of these problems by throwing money at them– paying somebody to walk the dog for us, or whatever. If I really had to, I could even buy out time at work, though that introduces other complications.
(To some degree, this is also self-inflicted– I don’t have to write another book at this time, or maintain this blog, but I choose to do so, and the time for those things takes away from time for other things. On the other hand, though, if I stopped doing the blog and social media (as I did for a while during the summer), I’d probably snap completely…)
Still, it’s a constant grind, and it cuts into what we know we should do. We’re well aware that we ought to feed SteelyKid homemade meals including fresh vegetables, but she refuses to believe that they’re food, and we’re generally too tired to fight about it. She’ll eat pre-made chicken nuggets without too much complaining, so that’s mostly what she gets. We know she ought to get plenty of exercise, and mostly we try to keep her active. But if parking her in front of the tv frees up time to dust off some class notes or answer some work emails, or just clean up a little so I’m not washing dishes at 10pm, well, at least we can usually get her to watch MythBusters which is kind of educational-ish.
The phrase “work-life balance” gets thrown around a lot in academia, which always seems sort of inappropriate. It’s not a balance, it’s a juggling act, hoping I can keep my class up in the air long enough to deal with stuff at home, and that the kids won’t get sick until after I get those papers graded, and deal with the latest Major Crisis. I realize that lots of people have it much worse than we do, and that’s indescribably depressing.
Burke’s response to this is also fascinating, picking up on some key issues about how these things get dealt with, both in the tongue-clucking tone of the NPR piece, and on a more official level:
This is a fairly established line of expert reasoning in national discourse about issues that have been coded or marked as “public health” crises. Using a fairly narrow range of methodologies drawn from social science, particularly economics and social psychology, the experts verify first that existing forms of public education have been sufficient to establish baseline awareness of a public health problem that turns on behavior. Sometimes they read the evidence and conclude that the education needs to be in a different form or in a different location, or that more money needs to be spent on it. Usually that involves experts in the expert’s community of peers, if the recommendation is taken.
Sometimes (as in this case) the experts conclude that there is sufficient awareness, just not sufficient compliance. People aren’t doing what they’re supposed to be doing with the near-ubiquity that they ought to do it: not wearing helmets or seat belts, not quitting smoking, not taking a recommended pharmaceutical, not getting enough exercise, not minimizing their consumption of some kind of mass media, not following dietary recommendations, and so on.
Rarely if ever does the community of experts pause at this moment to inventory their own histories of error and exaggeration, or ask what the nature of their relationship is to the publics they advise and the resources they demand for the advising and studying of those publics. That alone might provide something of a testable hypothesis: that sometimes publics stall and defer on doing the things they ought to do because at least some of them are old enough to remember other things that they were told they ought to do that later on turned out to be not so important, or actively the wrong thing to do. Or that some of the advice turns out to be improvident or unrealistic in unnoticed or unacknowledged ways. Or that the experts are being impatient: on some issues, it turns out that people will change, if you just quietly keep working on the problem and don’t insist on changing your focus and approach every three seconds.
This struck a chord because of a number of discsussions, at Science Online and on Twitter about the “Deficit Model” of science communication– the idea that people just need to hear the facts about what they ought to be doing one more time. In fact, this is a pretty dismal failure, for reasons that apply just as much to areas that are less science-y than things like climate change or vaccine hysteria. A lot of the time people know perfectly well what they should do, but they can’t or won’t for any of a wide variety of reasons. It might have to do with skepticism about official wisdom, as in the examples quoted above, or it might just be that they’re too tired and worn down to deal with the hassle of getting kids to eat right.
Burke goes on to make a terrific argument about the moral content of a lot of official approaches to this, and its fundamental lack of what for lack of space and a better word I’ll call empathy. I can’t really do it justice without quoting just about the whole thing, but I do want to get one more bit from near the end:
We are offered a thousand reasons to complain of other people’s behavior (and to excoriate and loath our own) on the grounds that it will cost us too much. That we should talk about what is good and bad, right and wrong, mostly in terms of the selfish consequences, or at best, in terms of the kind of closeted idea of a collective interest that neoliberalism dare not directly speak of–sort of the nation, sort of the economy, sort of the community, but really none of those directly or clearly.
What the experts generally rarely say is, “Because we care for one another, want the best possible lives for one another, and would not be deprived of each other’s company one moment sooner than we must”. Why does your mom tell you to wear a helmet and stop smoking and lose some weight? Ok, sometimes because of the ordinary psychodrama of family life and its little struggles for power, but sometimes, often times, simply because your mom or your dad or your kid or your friend loves you. Because they value you.
This humane sensibility drops from public policy and technocratic expertise because, for one, we’ve become profoundly unpracticed in its use.
This is where it comes back to the work-life thing for me (though it’s not the end of the post, and what comes after it is also excellent and important to read). We hear a lot of arguments, especially in academic science, that attempt to advocate for work-life balance or “family-friendly” policies in a manner that awkwardly frames them in terms of cost– that people would be healthier and more productive with such policies in place, or that not providing flexibility costs us by driving women out of the field. Those quickly get mired in all kinds of quibbling– arguing about whether the abstract gains are worth some abstract cost.
Ultimately, though, this stuff isn’t an economic issue or a gender issue– it’s a question of basic human decency. Both men and women should get the flexibility they need to care for their kids and themselves because that’s how people ought to be treated. Students and post-docs shouldn’t be expected to put in 80-hour weeks (at minimum), or have their dedication questioned because they have families or outside interests because that nonsense is fundamentally inhumane.
I’m too frazzled to really come up with a grand conclusion (I’m writing this while proctoring an evening exam, toward the end of a long and frustrating day). I’ll just note again that both of these pieces really resonated with me, for a lot of reasons, and leave it at that.
We’ve got calls out to the local grad programs, and I’ve mentioned this on Twitter a couple of times, but it can’t hurt anything to post it here as well: we’ve got a huge overabundance of first-year engineering students that is forcing us to open extra sections of our intro physics classes to accommodate them. The problem is, we don’t have people to teach the new sections. Thus, we are looking for an adjunct to take one section of intro physics or introductory astronomy labs, starting in April and running through mid-June.
If you’re within convenient distance of Schenectady, NY, and might be interested, please contact me as soon as possible– if we don’t find somebody by Monday, we’ll need to cancel some classes, and there will be wailing and gnashing of teeth. If you know somebody else who might be interested, have them contact me.
(NOTE:If you post a comment berating me for contributing to the adjunctification of academia, I will delete it. This is not a long-term penny-pinching move (well, not directly, anyway), it’s a short-term emergency measure. I am doing my best to hold the line against shifting regular teaching from tenure-track faculty to adjuncts, and don’t need to hear any further exhortations in that direction. This is an unanticipated emergency staffing shortfall, not something that would be addressed by a tenure track position in any case.)
There’s been a lot of bloggage recently about a new study in the Proceedings of the National Academy of Sciences indicating bias toward male students on the part of faculty who thought they were evaluating an application for a laboratory manager. Half of the faculty in the study were given an application with “Jennifer” at the top, the other half one with “John” as the first name, and both male and female faculty rated the male student more highly, and would offer the male student a higher salary. Sean Carroll and Ilana Yurkiewicz talk about the study and the results in more detail.
So, without getting into the details of the study, let’s say I’m convinced by this that gender bias is a problem in hiring. Presumably, this would extend up to the faculty level, as well, where it’s even more important to take bias out of the process because the stakes are higher in a lot of respects. Now, one possible solution to this would be to try to make the hiring process blind– as has been shown effective in orchestra auditions, for example. So, if you wanted to make the faculty hiring process gender-blind (or race-blind, for that matter), how would you do that?
(This is not entirely a theoretical question, by the way, as there’s a non-trivial chance we will be hiring a visiting faculty member in the very near future, and a tenure-track job is not out of the question down the road a bit.)
Obviously, you couldn’t do a blind search all the way through, because at some point you’re going to invite a few candidates to campus for interviews. But would it be possible to do the search blind up to that point?
At first glance, it seems like a Hard Problem, because the materials that figure into a typical search are kind of difficult to anonymize to the necessary degree. For a faculty search, we ask for some statements from the candidate about their teaching and research, a CV, and three letters of recommendation. The statements are not necessarily gendered– though we occasionally get “As a woman in physics, I…” statements, those could probably be avoided. The CV is somewhat more problematic– while you could blank out the name at the top, the really important thing is the publication list, and there it’s a little hard to avoid identifying the author who is common to all the papers. It’s not a complete show-stopper– a lot of authors are identified only by initials, not full first names– but it’s trickier. Reference letters are the hardest of all, unless the people writing the letters are extremely scrupulous about avoiding gendered pronouns– in fact, we have sometimes had to resort to the reference letters for the purpose of identifying the gender of applicants with unusual names when we’re required to give some accounting of the number of applicants from underrepresented groups at the conclusion of a job search.
None of those are completely insurmountable– if the requirement were stated in the job ad, you could probably get the letter-writers to search-and-replace “Firstname” with “Dr. Lastname,” and the publication lists are primarily used to count the number of publications and the journals where they appeared, and only rarely does anyone look them up to be able to check the names. In the spherical frictionless world where support departments are supportive, you could probably have somebody in Human Resources transcribe any necessary documents and remove gender references. (We’ll pause here to give those who have had contact with real-world Human Resources offices a chance to stop laughing and collect themselves.) It’s not impossible, but the extra resources required make it seem like a bit of a hard sell.
I assume somebody must have thought seriously about this before, though I don’t have the time to Google for it. If there’s an effective and relatively simple way to go about this, though, I’d love to hear it. Even better would be a way to convince other faculty of the need for going through whatever additional hassle would be involved.
(The alternative, of course, is to be even more gender conscious, and make an affirmative effort to give extra consideration to applications from women (and other underrepresented groups, if we extend this to include other factors). I am somewhat skeptical that this would really be effective, though, as the judgments involved are inherently kind of subjective and prone to the semi-unconscious decision making that Kahneman talks about. And it’s not like we’re not already getting a lot of exhortations to hire from underrepresented groups– we’re aware of the general problem. I’d have more confidence in something that eliminated the possibility of bias at an earlier stage, shifting the effort involved to somebody else.)
(Obligatory disclaimer: Nothing in the above should be taken as a statement of institutional policy or a binding commitment to do anything in particular when next we hire. This is personal opinion and speculation, nothing more. If this post generates suggestions that seem workable and fit within existing institutional policies and applicable state and federal laws, I may try to implement them. My ability to force any kind of policy change is pretty much nonexistent, though, so I can not promise anything.)
Somebody on Twitter linked this article about “brogrammers”, which is pretty much exactly as horrible as that godawful neologism suggests. In between descriptions of some fairly appalling behavior, though, they throw some stats at you, and that’s where it gets weird:
As it is, women remain acutely underrepresented in the coding and engineering professions. According to a Bureau of Labor Statistics study, in 2011 just 20 percent of all programmers were women. A smaller percentage of women are earning undergraduate computer science degrees today than they did in 1985, according to the National Center for Women in Technology, and between 2000 and 2011 the percentage of women in the computing workforce dropped 8 percent, while men’s share increased by 16 percent.
Specifically, that last sentence. How is that even possible? The only way I can see for that number to make sense is if at least 8% of the workforce in 2000 didn’t provide gender data, but if that’s the case, you can’t really say anything sensible about changes in those numbers. You could probably arrange some distribution in which those figures are percentages of percentages (that is, the fraction of women started at some value, and decreased to 92% of that original value), but it would require the initial fraction of women to be higher than the initial fraction of men (so that the same decrease in absolute terms is a larger percentage for men than women), which is obviously not the case.
Of course, being traditional journalists, they don’t make it easy to find the source. They sorta-kinda attribute it to the National Center for Women in Technology, whose scorecard makes a similar claim for 2000-2009, though the disparity is smaller (11% and 13%). They source it to the Bureau of Labor Statistics, but the relevant-seeming tables don’t break out men and women separately, so I can’t see how you would get a bizarre claim like the quoted bit. If you want to know the male participation in these industries from those BLS tables, the only thing you can do is subtract the women’s percentage from 100, which obviously cannot give you a different change in the fraction of men than the fraction of women. Unless they’re working from some more complete data set with more fine-grained demographic information, but if so, it’s not obvious where you would find that.
I have too much other stuff to do to keep trying to track this weirdness down, but this is going to bug me all day, so I’ll throw it out to my readership in hopes that one of you either knows how you would get such an obviously weird result, or can track down an explanation with superior Google-fu.
The American Astronomical Society (AAS) Employment Committee is hosting a panel discussion at our annual AAS winter meeting in Austin on current issues related to the postdoc job market, with a focus on the increase in post-doc type positions without a corresponding growth in potential permanent academic positions. The session will be on Wednesday, January 11th from 10-11:30 am. They are particularly interested in including the perspective of those who have switched from the astronomy/astrophysics track into a non-academic track.
Please contact the panel organizer (Fred Rasio (rasio at northwestern dot edu)) if you have an astronomy/astrophysics PhD, held an astronomy/astrophysics postdoc position, and then went on to pursue a non-academic career route. Since there is no funding for transportation costs, likely this will be easiest for those already in the Austin area.
This is outside my area, and may be outside the area of most of my readers, but I’ll give it what signal boost I can, and maybe some of the more astronomy-focused blogs can get it in front of the right people.
A currently popular explanation for the increasing price of higher education is that all those tuition dollars are being soaked up by bloated bureaucracy– that is, that there are too many administrators for the number of faculty and students involved. While I like this better than the “tenured faculty are greedy and lazy” explanation you sometimes hear, I’m not sure it’s any more valid. In part because proponents make it difficult to see if it’s any more valid.
One of the major proponents of the administrative bloat idea is Benjamin Ginsberg, a political scientist at Johns Hopkins, who is flogging a book making this argument, and has a long piece in Washington Monthly about it (that link may spawn a “print” window, but it’s the only way to get the whole thing on one page without their incredibly annoying animated and pop-up ads). I’ve sat in enough pointless administrative meetings that I’d really like to believe this, but at least in the Washington Monthly piece, he’s engaging in the sort of argument-by-factoid that is frustrating when it comes from professional pundits, and vastly more irritating coming from a professional academic.