A bit belated, but fun – I went as Rear Admiral Grace Hopper this Halloween:
And yes, I did have a few microseconds to share.
In my last post, I described how the experiences of women in CS have changed historically. In this post, we saw that the academic side of computer science is a relatively recent thing. For this post, I’d like to focus some more on that aspect of the history. Like that last post, this post will be specifically focusing on North American CS (we’ve seen previously that female participation in CS is different outside the West!).
Generational differences exist between female scientists in academia. Etzkowitz et al in a 1994 paper found differences in experiences and values between the trailblazing “First Generation” of women in a field, and the subsequent “Second Generation”. As the paper is now 20 years old, it’s not too surprising that it feels a bit out of date – what comes after the Second Generation? (Another dated thing about the paper is that CS is described as being as female-friendly as biology.)
The Etzkowitz et al paper studied 30 academic science departments (biology, chemistry, physics, CS, and electrical engineering). They went into the study interested in the notion of critical mass – whether having enough women in a department would lead to a positive feedback cycle leading to gender equality. (Answer: it’s not that simple.) In the process of studying critical mass, they found the women who had entered the field before it was attained (First Gen) had fundamentally different experiences than the women who entered after.
The trailblazing women who entered CS – or similar disciplines – when there were no other women in their departments learned to cope with the culture by adopting the “male model” of a scientist. These women generally did not have families, and for those that did, it took a clear backseat to their scientific careers.
In departments without other women, these trailblazers often encountered blatant sexism and harassment. This open sexism did not abate until a critical mass of women was reached and women not only had “safety in numbers” but men were more aware that this behaviour was inappropriate. Etzkowitz et al describe the critical mass as a “strong minority of at least 15%”. Note that in this statistic, they are referring to how many women are faculty and graduate students in a department – this does not include undergrads.
These trailblazers were often uneasy about forming Women in Science type clubs, sometimes refusing to participate out of fear of stigmatization by their male colleagues. These women, having fought tooth and nail for any status and accomplishments they have, were sometimes afraid that association with women’s movements would devalue their achievements. Instead of being viewed on par with the other men, they worried they would be judged only in the “women’s track”. (There are certainly women who have gone against this – Maria Klawe would be a clear example of a First Gen computer scientist who has been promoting women in CS clubs and conferences.)
A quote from the paper really sums up the First Gen – one senior female scientist participating in the study described her generation: “The ones who did [science] were really tough cookies. Now it’s easier to get in. At one time it wasn’t even acceptable to start. So if you started back then you were tough to begin with. I have quivering women coming through who are very smart asking can they compete with men, and can they compete on a very competitive, fierce playing field. Of course they can. They just are not taught to be competitive. They don’t expect to win. The reason why I am successful is because I never felt this way.”
Competitiveness was a large source of tension between these women and the Second Generation. In the mind of the First Gen, women need to adapt themselves to the man’s word – and need to be competitive. Second Gen women have instead favoured trying to change the culture to allow women who meet cultural notions of femininity: making the culture more friendly and collaborative.
Women who entered CS after critical mass was achieved had a very different experience coming into the field. Etzkowitz et al don’t provide timelines in their paper; from talking to female faculty in my department, I’d guess that this generation begins with the women who entered CS as undergraduates in the 80s.
These women tended to have high expectations about the (First Gen) female faculty in their departments, wanting their moral support and guidance for coping in a male-dominated culture. Often, they were disappointed. The Second Gen women wanted to have it all: to be women and scientists – and the First Gen women failed as role models in this regard.
For the Second Gen women who had First Gen women as advisors, there was tension. One Second Gen participant described: “[having a woman advisor] turned out to be somewhat of a mistake. I was under the impression that having a woman adviser would make life a bit easier… It turned out to be worse… Their motto is sink or swim… My adviser’s approach was to put it too far out of my grasp.“
First Gen women, as advisors, were extra hard on their female advisees, “to prepare them to meet the higher standards that they would be held to as women.” And as advisors, the First Gen women felt unable to help their advisees; as one participant put it, “They ask me when they should have children, can I take a part-time post-doc and then get back in? I don’t know [the answers]. I can’t help them.”
Most of the Women in CS/Science initiatives appear to have been started by Second Gen women, partly in response to the unhelpfulness of the First Gen women in terms of advising them about work-life balance and coping with a hostile, isolating work environment. And many Second Gen women left academia to look after their families, convinced that they would not be able to do both – if an academic career required conforming to the man’s world like the First Gen did, they decided they did not want to be a part of it.
As I noted already, the Etzkowitz et al paper was published 20 years ago. I took my first CS course in 2007, and for me and my cohort it was a very different experience than that of the Second Gen women. Approximately 20% of the CVS faculty at my alma mater are women, predominantly women of the Second Generation. They have families and the Focus on Women in Computer Science club was (and still is) highly visible and active. Personally, I’ve received a lot of invaluable mentorship and advice from Second Gen women.
My generation is far removed from the overt sexism that the First Gen experienced, and we don’t appear as worried about balancing a career with family. For a lot of us, these feel like problems of the past. Occasionally I’ll hear friends comment about Women in CS events that “I feel like the women running this are trying to make up for what they didn’t have when they were our age rather than what our generation wants.” The best Women in CS events seem to be the ones that take generational differences into account.
Growing up, girls of my generation performed equally well in science and math as boys (sometimes outperforming). For a lot of us – though hardly all – there was no expectation setting foot in a CS class for the first time that it would be unfriendly to women. My experience of undergraduate CS was that of a collaborative field. Personally, it wasn’t until graduate school that I felt I encountered gender-based barriers.
But despite many improvements in the culture, female enrollment in CS hasn’t improved a whole lot since hitting that 15% critical mass. Despite an uptick in the mid-80s, the numbers are now down to around 18%. Clearly, critical mass isn’t enough on its own to get female participation to 50%.
For biology, however, the numbers have been increasing – 53% of biology doctorates in the US in 2009 were given to women (Zuk & O’Rourke). (I’ve posted previously about why biology has more women than CS.) But as Zuk and O’Rourke caution: “First, demography alone has not solved the problem [of gender inequality] in the past. We frequently make presentations about gender and science to young audiences; since perhaps the early 1990s, a common response from graduate students to the concern about lack of female professors is that “their” cohort had not yet gone through the system. In other words, the students optimistically suggested, all we needed to do was wait for them to move into the academic job market in equivalent proportion to their numbers. Unfortunately, that has not occurred over the past few decades, and it is not likely to happen now. Although the landmark majority of female biology Ph.D.’s was reached only recently, the number of women in undergraduate and graduate programs in the life sciences has been increasing for the past several decades.”
Subtle, social-psychological barriers still remain in the scientific community (see: Moss-Racusin et al, Steinpreis et al, Knobloch-Westerwick et al, Heilman et al). It’s unlikely that biology or any other science will get to having 50% female faculty until these barriers are gone. In a previous post I talked about how the key to changing stereotypes about women is to get people to see women as heterogeneous – generational differences are just one way that women in CS are heterogeneous.
Female participation in computer science in North America has varied a great deal over time. Women were the original “computers” before the days of computing machines – and then were hired as the low-status “coders” to run those machines. Over time, coding/programming was more widely recognized to be difficult – and it was shifted from being “women’s work” to “men’s work”.
When computer science emerged as an academic discipline in the 70s and 80s, women were well-represented (30-40%). As enrollments in CS programmes exceeded what departments could manage, they tightly restricted the paths one could take into a CS major – unintentionally pushing non-traditional students like women out of the field. A big lesson from that period is that non-traditional students come from non-traditional paths – many of these women were starting in majors such as psychology or linguistics, or transferring from community colleges, and hence did not follow the “standard” path into computing careers.
Our view of women in computer science begins with the history of women in academia. The 19th century marked the rise of women’s colleges in the United States [1] as policies barring women from education were loosened. Women campaining for access to higher education did so on an argument that it would “produce better wives and mothers’’ for Americans [1]. For women of privilege in American society, a basic understanding of science and math in turn became “necessary for motherhood.’’
It should be emphasized that this was a trend for white women of privilege – most women who studied science in the 19th century were the daughters of scientists and other intellectuals.
For the women scientists that emerged from these colleges, there were few job opportunities. Teaching at the women’s colleges was the main possibility [1]. Working as a “computer’’ was another possibility. Women pursuing PhDs or faculty positions were expected to be single or “in no danger of marrying’’; marriage meant resigning from the programme or their job [1]. As time progressed and society progressed, women in these positions began to feel they could be both wives and scientists – when they resisted the norm of resigning upon marriage, they were met with opposition: they were threatened and usually fired [1].
1870-1900 marked an era of slow infiltration: women began entering doctorate programmes at traditional (male) institutions in countries such as the US and Germany [1]. Most universities were hesitant to allow the women into the PhD programmes, but would instead admit them as “special students’’ and give them additional bachelor’s degrees at the end of their studies. While by 1910 women were starting a presence in science at traditional institutions, there was no equality in employment, and jobs remained deeply sex typed.
With the slow rise of women in science came the corresponding rise of “women’s work‘’ in science. So-called women’s jobs typically were “assistants’’ to scientists, or working as computers for larger groups. These women were systematically ignored in the larger scientific community, left out of lists of scientists, conferences, and histories [1]. Indeed, from 1911 onward there were overt efforts to reduce the numbers of women in science, even with their roles undervalued [1].
It should be emphasized that computation was considered “women’s work’’ in the 19th and early 20th century. Looking at the history of the biological and social sciences in this time, quantitative methods were considered “low’’ enough that women could do them – but qualitative methods required “the intellect of a man’’ [2]. The reversal of the status (and gendering) of quantitative vs. qualitative work in the social and biological sciences happened well into the 20th century (sometime between the 30s-50s) [2].
By the 1920s, women in academia were still largely kept to the women’s colleges [1]. The colleges, however, allowed a place to organize campaigns for change. Women began fighting for access to education using evidence from psychology and anthropology that women too were capable of science and math [1].
The 20s and 30s marked an expansion of government-employed scientists, who were assigned “women’s work’’ (assistants, computers, etc) and were grossly underpaid and undervalued [1]. The World Wars increased the scope of “women’s work’’ as labour shortages necessitated it. By 1938, the numbers of women working in scientific and technological roles for the US government had dramatically increased – despite overtly hostile job conditions [1].
The World Wars also marked the birth of digital computing. Computing machines were devised in the UK for cryptographic purposes. These machines, and the hand computations done in the wars throughout the world, were commonly performed by women. ENIAC, arguably the first real computer, was announced in 1946. The plan to run the ENIAC was such: a male scientist would be the planner, deciding what was to be computed – and a low-rank, female “coder’’ would do the actual machine coding [3]. These “Eniac Girls” and the other female machine operators of their time have been frequently forgotten in the history of science; at the time they were not seen as important and it is really only in recent decades that their work has been recognized.
Grace Hopper, who worked on the ENIAC, later described programming as “it’s just like planning a dinner. You have to plan ahead and schedule everything so it’s ready when you need it. Programming requires patience and the ability to handle detail. Women are ‘naturals’ at computer programming.” [4]
What was not anticipated was that the coding would actually be difficult [3]. As computers began being used for commercial purposes in the 50s, a labour shortage emerged. The status of being a programmer rose; as the difficulty of its task was recognized, the assumption that it should be done by men took over. Computing in the 50s and 60s can be characterized by a large, shotgun approach to recruiting “good programmers’’ with little knowledge of what a “good programmer’’ was [3]. Programming began to be seen as a “dark art’’, and programmers began to be seen as asocial [3].
As computer programming rose in prominence, it became masculinized. Women were still allowed entry to the jobs due to the desperation for quality labour. However, lazy hiring practices that focused on spurious aptitude and personality tests hurt female participation in the industry [3]. Inconsistent professionalization efforts also hurt female participation by restricting what it mean to be a programmer [3]. The men running the show simply did not consider how their hiring practices discriminated against women.
Computer programming stayed largely independent from academic computer science. In the 50s and 60s, computer science was conducted through other departments, typically as a hobby or side-project [3]. The first CS classes were offered in the 60s, as the discipline struggled to assert itself as a discipline of its own [3].
By 1969, MIT had opened an undergraduate programme in CS – and the 70s marked the beginning of bachelor’s degrees in CS offered typically through electrical engineering or mathematics [3]. It would not be until the 80s, though, that CS programmes moved into their own departments.
From the start, computer science seemed like a “grab bag of various topics’’ related to computers [3] and attempts to define the discipline were inconsistent. Was computer science about information? Analysis? Algorithms? No consistent narrative was established, though algorithms eventually became dominant. This inconsistent narrative continues to be a difficulty in public outreach for computer science.
The opening of CS departments in the 80s provided a fertile ground for women. Women were increasingly studying the sciences in the 80s [5] – and academic CS had a relatively unentrenched culture. Women of the time flocked to CS in what is now seen as a golden age of female participation in the field. 37% of American CS degrees in 1985 were awarded to women [5]. In my next post, I’ll talk about how the experiences of these were different than the previous generations of women in CS. (Edit: the generational differences post is here)
The early 80s were also a boom-time for student enrollment in CS [6], which was linked to the rise of the personal computer. Personal computers had not been available until the late 70s; prior to then, computer science was hence only pertinent to academia, military, and business.
However, by the late-80s, enrollments began dropping – and disproportionately so for women [7]. The decline was “largely the result of explicit steps taken by academic institutions to reduce computer science enrollments when it became impossible to hire sufficient faculty to meet the demand.’’ [7] Steps included adding new GPA requirements for entering CS programmes, requiring more prerequisites, and retooling first-year CS as a weeder course. These actions disproportionately hurt not only female participation in the field, but participation of racial minorities as well. These “non-traditional’’ students had disproportionately come to CS via non-traditional paths (such as via psychology or linguistics) and disproportionately lacked the prerequisites as a result. The retooling of first-year CS as a weeder course also resulted in a competitive atmosphere that deterred many women.
The personal computer also led to further masculinization of computing [8]. Five reasons thought to have reduced female participation in the 90s were: the rise of video games, subsequent changes in stereotypes/perceptions of computing, the encouragement of boys to go into the field and not girls, an inhospitable social environment for women, and a lack of female role models [8].
The birth of the World Wide Web in the 90s and its spread beyond academic/military use led to a second bubble in CS enrolments. The hype of the dot-com bubble and the promise that a CS degree would lead to easy prosperity
led to a resurgence in enrollments in the late 90s. The dot-com bubble burst in 2000 – and enrollment with it a few years later [6]. Indeed, the NASDAQ has been found to be a predictor of CS enrolment at Stanford [9]. The perception of CS jobs as being volatile has also been implicated as a reason why women are deterred from CS careers [10].
The boom-time in the late 90s and early 00s led to a return of strict enrolment controls and a spree of hiring more CS faculty [6]. These boom-times also reduced the amount of service teaching: with CS programmes overburdened, CS departments had few resources and little motivation to teach non-CS students. At some universities, departments such as physics or math began offering their own CS classes to their own students – leading to CS becoming increasingly isolated from the other sciences – and from non-traditional students.
When the bubble burst, the “get-rich-quicker’’s disappeared – and CS departments were left trying to get more “bums in seats’’. Enrolments did not recover again until the mid 00s – and have been on the rise since [6]. Overall, a pattern of cyclical enrolment emerges. Boom times lead to more students, then more enrolment controls; bust times lead to more outreach. Bust times also result in disproportionately many women leaving the field, or not going in at all [6] – indeed, as of 2011, 18% of CS students are female [5].
Enrollments in CS are now skyrocketing again: the 2012 Taulbee Survey found that CS enrollments have risen for the fifth straight year [10]. Facing packed classrooms and overburdened teaching resources, some CS departments
are once again considering cutting their interdisciplinary programmes and service courses. Hopefully this time around we’ll have learnt from the past.
References