{ women in cs }

  • A Reading List for Critical CS Education

    One of the challenges of studying equity in computing is that while there is a lot of work on the subject, the topic lacks the infrastructure of a well-established (sub)field. For example, when I was a PhD student studying for my qualifying exam, there was not a pre-established list of texts for me to study. When students and colleagues ask me for readings on equity in CS I tend to start from scratch each time, and will miss things.

    To solve these issues, I’ve put together a reading list on critical prespectives on equity in computing. The list is heavily annotated for guidance, and importanty covers foundational texts in areas that a criticial approach to computing equity should draw on (e.g. critical pedagogy, Science and Technology Studies, Gender Work & Organization, critical disability studies, critical race studies).

    I welcome feedback on the list. Hopefully you’ll find it useful!

  • What CS Departments Do Matters: Diversity and Enrolment Booms

    I’ve written before about the historical factors that have led to the decline in the percentage of women in CS. The two enrolment booms of the past (in the late-80s and the dot-com era) both had large impacts on decreasing diversity in CS. During enrolment booms, CS departments favoured gatekeeping policies which cut off many “non-traditional” students; these policies also fostered a toxic, competitive learning environment for minority students.

    We’re in an enrolment boom right now so I — along with many others — have been concerned that this enrolment boom will have a similarly negative effect on diversity.

    Last year I surveyed 78 CS profs and admins about what their departments were doing about the enrolment boom. We found that it was rare for CS departments to be considering diversity in the process of making policies to manage the enrolment boom.

    Furthermore, in a phenomenographic analysis of the open-ended responses, I found that increased class sizes led many professors to feel their teaching is less effective and is harming student culture (this hasn’t been published yet — but hopefully soon!)

    Around the same time I put out my survey, CRA put out a survey of their own on the enrolment boom. Their report has just come out; they have also found that few CS departments are considering diversity in their policy making — and that the departments who have been considering diversity have better student diversity.

    From CRA’s report:

    The Relationships Between Unit Actions and Diversity Growth

    The CRA Enrollment Survey included several questions about the actions that units were taking in response to the surge. In this section, we highlight a few statistically significant correlations that relate growth in female and URM students to unit responses (actually, a composite of several different responses).

    1.    Units that explicitly chose actions to assist with diversity goals have a higher percentage of female and URM students. We observed significant positive correlations between units that chose actions to assist with diversity goals and the percentage of female majors in the unit for doctoral-granting units (per Taulbee 2015, r=.19, n=113, p<.05), and with the percent of women in the intro majors course at non-doctoral granting units (r=.43, n=22, p<.05). A similar correlation was found for URM students. Non-MSI doctoral-granting units showed a statistically significant correlation between units that chose actions to assist with diversity goals and the increase in the percentage of URM students from 2010 to 2015 in the intro for majors course (r=.47, n=36, p<.001) and mid-level course (r=.37, n=38, p<.05). Of course, units choosing actions to assist with diversity goals are probably making many other decisions with diversity goals in mind. Improved diversity does not come from a single action but from a series of them

    2.    Units with an increase in minors have an increase in the percentage of female students in mid- and upper-level courses. We observed a positive correlation between female percentages in the mid- and upper-level course data and doctoral-granting units that have seen an increase in minors (mid-level course r=.35, n=51, p<.01; upper-level course r=.30, n=52, p<.05). We saw no statistically significant correlation with the increased number of minors in the URM student enrollment data. The CRA Enrollment Survey did not collect diversity information about minors. Thus, it is not possible to look more deeply into this finding from the collected data. Perhaps more women are minoring in computer science, which would then positively impact the percentage of women in mid- and upper-level courses. However, units that reported an increase in minors also have a higher percentage of women majors per Taulbee enrollment data (r=.31. n=95, p<.01). Thus, we can’t be sure of the relative contribution of women minors and majors to an increased percentage of women overall in the mid- and upper-level courses. In short, more research is needed to understand this finding.

    3.    Very few units specifically chose or rejected actions due to diversity. While many units (46.5%) stated they consider diversity impacts when choosing actions, very few (14.9%) chose actions to reduce impact on diversity and even fewer (11.4%) decided against possible actions out of concern for diversity. In addition, only one-third of units believe their existing diversity initiatives will compensate for any concerns with increasing enrollments, and only one-fifth of units are monitoring for diversity effects at transition points.
    From a researcher’s perspective this has me happy to see: we used very different sampling approaches (they surveyed administrators, I surveyed professors in CS ed online communities), we used different analytical approaches (their quantitative vs. my qualitative), and we came to the same conclusion: CS departments aren’t considering diversity. This sort of triangulation doesn’t happen every day in the CS ed world.

    CRA’s report gives us further evidence that CS departments should be considering diversity in how they decide to handle enrolment booms (and admissions/undergrad policies in general). If diversity isn’t on policymakers’ radars, it won’t be factored into the decisions they make.

  • Helping women in CS with impostor syndrome is missing the forest for the trees


    Alexis Hancock recently wrote an article on impostor syndrome that has been on my mind ever since, as it adds so nicely to a blog post I wrote several months ago. I wanted to try and explain why so many women have impostor syndrome in CS:

    Sociologists like to use performance as a metaphor for everyday life. Erving Goffman in particular championed the metaphor, bringing to light how our social interactions take place on various stages according to various scripts. And when people don’t follow the right script on the right stage, social punishment ensues (e.g. stigma). […]

    Since not following the script/game is costly for individuals, we’re trained from a young age to be on the lookout for cues about what stage/arena we’re on and what role we should be playing. […]

    Impostor syndrome is the sense that you’re the wrong person to be playing the role you’re in. You’re acting a role that you’ve been trained in and hired for – but your brain is picking up on cues that signal that you’re not right for the role.

    When [people] go on to play roles [they haven’t been raised for], they still sometimes encounter social cues indicating they’re in the wrong role. Impostor syndrome results.

    Impostor syndrome is thought to be quite common amongst women in science. In this light I don’t think it’s surprising: there are so many cues in society that we are not what a ‘scientist’ is supposed to look or act like. We don’t fit the stereotypes.

    I’m far from the first person to argue that impostor syndrome comes from environmental cues. What Hancock’s article does is point out the contradiction: impostor syndrome has environmental causes, but is talked about as being an individual’s personal problem.

    [While struggling with impostor syndrome] I became consumed with proving myself. Still, all the advice I received came in the form of a pep talk to “believe in myself” again. This common response to the struggles of women in tech reinforces the idea that imposter syndrome is the ONLY lens to view and cope… but the truth is, our negative experiences in tech are usually outside of our control. The overwhelming focus on imposter syndrome doesn’t provide a space to process the power dynamics affecting you; you get gaslighted into thinking it’s you causing all the problems.

    Similarly, Cate Hudson writes that:

    Yet imposter syndrome is treated as a personal problem to be overcome, a distortion in processing rather than a realistic reflection of the hostility, discrimination, and stereotyping that pervades tech culture. […] Assuming that it’s just irrational self-doubt denies potentially useful support or training. Most of all, chalking up myriad factors to such an umbrella term belies the need to explore where these concerns arise from and how they can be addressed or mitigated. Subtle or not-so-subtle undermining behavior by colleagues? Gendered feedback? Lack of support or mentorship? […] We pretend imposter syndrome is some kind of personal failing of marginalized groups, rather than an inevitability and a reflection of a broken and discriminatory tech culture.

    So many well-intentioned diversity efforts in computer science focus on impostor syndrome and try to help women cope with it. But that discourse treats the women who have impostor syndrome as though they have an individual problem. The effect can silence women: instead of seeing their negative environment as a structural issue, they blame themselves.

    Those of us who want to get more women into CS need to stop telling women that they suffer from impostor syndrome and instead help them see environment they’re in. The social cues that are affecting them need to be identified and mitigated. And we need to stop teaching women to blame themselves for the sexism around them.

  • Women in Computing As Problematic: A Summary

    I’ve long been interested in why, despite so much organized effort, there percentage of women in CS has been so stagnant. One hypothesis I had for some time was that the efforts themselves were unintentionally counter-productive: that they reinforced the gender subtyping of “female computer scientist” being separate from unmarked “computer scientists”.

    I was excited earlier this week when Siobhan Stevenson alerted me to this unpublished thesis from OISE: “Women in Computing as Problematic” by Susan Michele Sturman (2009).

    In 2005-6, Sturman conducted an institutional ethnography of the graduate CS programmes at two research-intensive universities in Ontario. In institutional ethnography, one starts by “reading up”: identifying those who have the least power and interviewing them about their everyday experiences. From what the interviews reveal, the researcher then goes on to interview those identified as having power over the initial participants.

    Interested in studying graduate-level computer science education, she started with female graduate students. This led her to the women in computing lunches and events, interviewing faculty members and administrators at those two universities. She also attended the Grace Hopper Celebration of Women in Computing (GHC) and analysed the texts and experiences she had there. Her goal was to understand the “women in computing” culture.

    In the style of science studies scholars like Bruno Latour, Sturman comes to the organized women in computing culture as an outsider. As a social scientist, she sees things differently: “Women in the field wonder what it is about women and women’s lives that keeps them from doing science, and feminists ask what it is about science that leads to social exclusion for women and other marginalized groups”

    Why I’m writing this summary

    Sadly, the thesis was never published. Sturman has since left academia and presently works as a high school teacher. I think the CS education community would benefit from hearing her findings. It’s worth noting upfront that Sturman is a poststructuralist: her goal is to problematize and deconstruct what she sees – not to test any hypothesis.

    The thesis is not an easy read. It’s a whopping 276-page read and took me about four hours. If you want to read it, but don’t want to read the whole thing, I suggest reading the last two chapters.

    I feel I only managed to get through it because I’ve taken courses from OISE on social theory. The thesis is extremely theoretical, and assumes the reader is fluent with the works of Dorothy Smith, Chandra Mohanty, Michel Foucault, Pierre Bourdieu, Stuart Hall, Bruno Latour, Thomas Kuhn, Evelyn Fox Keller, Sandra Acker, Judith Butler, Carol Gilligan, Donna Haraway, Judy Wajcman, and Simone de Beauvoir.

    Because the thesis has never been cited and it presents a very valuable perspective, I’m going to spend this blog post summarizing her findings. Where possible I’ll try to use Sturman’s own words, to foreground her own analysis.

    Women in Computing Lunches in the 80s

    The formal women in computing (WIC) lunches that Sturman attended have an interesting history. Female graduate students in the 80s felt the need “to come together for communication and support” amidst a “chilly climate” in their departments.

    For context, the 80s were a time when feminists were turning their attention to academia; women were organizing groups to draw attention to “women’s issues”, such as campus safety, sexual harassment, and workplace discrimination. These efforts were rooted in second wave feminism.

    The informal lunches and dinners in the 80s were “a student-initiated intervention to ‘help people in the program’”. As time went on, these female CS grad students became more activist. Some of these women went on to “present a report to the university about improving the ‘climate’ for women in science. Among the local concerns for women in Computer Science at the university was building at campus safety at night when they worked late in labs”.

    Women in Computing Lunches in the 00s

    Sturman spends a fair bit of time contrasting the lunches from the 80s with their form as she saw them in 2005-06. These lunches were now formal gatherings, organized by computer science departments. They had become institution-led rather than student-led, with a goal of keeping women enrolled in CS.

    The faculty who organized the lunches described the goal as “to create community …. [she] recalled her own experience as a graduate student when the few women in Computer Science and Engineering bonded informally in order to actively improve the chilly climate for women in their departments (Prentice, 2000). However, the institutional creation of ‘community’ sets formal boundaries that both constraint [sic] and enable practice.”

    Many of the graduate students in the 00s felt they had “nothing in common” with the other women at these lunches: “there was no inherent commonality to the group based on gender, other than the fact that they were all women, and all together ‘in the same room’. …. The diversity of their countries of origin, their educational backgrounds, their family responsibilities (or not) and many other aspects of their lives often made the idea of shared ‘experience’ seem alien to them.’

    The concerns of the women at these lunches was also different than in the 80s: discussion focused on individual choices and satisfaction, rather than workplace conditions. Rhetoric at the lunches was consistent with what we’d now call “Lean In” culture: trying to improve the psychology of the women there (promote yourself! etc) than make structural change.

    Ironically, these lunches “often had a negative effect upon [the female grad students’] self-perceptions and upon their hopes to help implement change” – the lunches did not “encourage graduate student participation in any comprehensive plan to change the male-dominate ‘culture of computing’ […] Rather, it seemed to students that the existence of the group and the funding of ‘get-togethers’ served as the main commitment of the department (and of university administrative resources) to gender equity, though both universities were concerned abut the declining enrolment of both male and female students in undergraduate Computer Science.”

    The female grad students in the 00s who did want to make change at their institutions instead found other avenues, such as undergrad affairs committees, or labour unions, more productive than the WIC lunches. This work, and that of the female faculty running the WIC lunches, was seen sometimes a burden by the women doing it. It took time and emotional energy for which they were not compensated. Many of the female students noted that they had to be careful how much work they did promoting women in CS; if they were seen as doing “too much” they anticipated it would hurt their job prospects.

    As CS departments embraced numerical-based goals for diversity, the female graduate students felt like they were used by their institutions to meet these institutional diversity goals: “To S and some of the other students … the ‘women in computing’ groups seemed a position of powerlessness. In such a position, they felt subjectified as ‘women in computing’ for special interest from the university but with little direct power in effecting institution change where gender inequity was identified”.

    As for the community building, one participant stated that “I don’t really feel a huge need for a sort of a… support group”. The lunches were seen more as a networking opportunity. Some women “disidentified with the problems other women talked about, and were depressed by or disinterested in warnings about ‘toxins’ that they as yet did not detect”. As one participant put it, “the stories [at the lunches] will just depress me [if I go].”

    The young women were often ambivalent about these lunches and other WIC initiatives, feeling uncomfortable about receiving “special treatment”. One participant worried it would seem “‘unfair’ to her male colleagues”. Other women described receiving “special treatment” in as undermining their identities as independent and self-made computer scientists.

    Women’s Work in Computing

    Participating in women’s lunches and presenting research at women’s conferences contrasted with the “gender-neutral” activities that women in CS must also partake in. Women felt pressure both to go to ordinary (male-dominated yet “gender-netural”) conferences as well as to present research at the women’s conferences.

    As Sturman put it: “for the women graduate students in Computer Science who participated in this study, the university practice of ‘gender equity’ means negotiating the contradictory position of attending to institutional demands for individuated ‘gender neutral’ scholarly performance in competition with peers for external and internal awards, jobs and research recognition while at the same time being hailed to affiliate with the ‘community’ of ‘women in computing’. Through these intersecting discourses, for many of these students, the sign of ‘feminism’ seems more a set of institutional rules and boundaries for gender performance and identity management than a relevant activist project.”

    At the same time, Sturman observed that certain subfields and activities in CS have become seen as “female-friendly” because they are seen as having more social relevancy and collaborative work. These areas include software engineering, human-computer interaction, computational linguistics, as well as the practice of teaching. Sturman also noted a perception of these areas being less mathematical, reflecting gender stereotypes that women don’t like math.

    Sturman challenges the categorization of these areas as “female-friendly” – the assumption that social relevancy and collaboration (and less math) are suitable for women reflects outdated and harmful stereotypes of women.

    The institutional culture at both universities Sturman studied valued the more masculine, abstract subfields of CS. Areas like SE and HCI were not seen as “real” computer science and given less respect. Teaching-track faculty were not seen as doing as contributing equally to the department as research faculty. Indeed, teaching track jobs were described as more suitable for women so they wouldn’t have to be burdened by research and “you look after your children”.

    Work/Life Balance as Problematic

    The discourse of “work-life balance” appears frequently in the women in computing arena. Sturman viewed this in terms of gender performance: “[a female grad student being interviewed talked] as she showed me pictures of herself and her boyfriend on a camping trip as evidence of her successful performance of ‘work-life balance’. The successful management of ‘work-life balance’ is a workshop topic at many ‘women in computing’ events. In contradictory interaction with the intensification of institutional performance of the self, it is often an almost impossible feat for young women (and men) who are entering the CS/IT workforce, whether in the academy or in industry. Proof of the accomplishment of work-life balance, or of attempts to self-improve in that direction, is an important part of ‘women in computing’ discourse.”

    Furthermore: “The illusory goal of work/life balance … moves ‘women in computing’ discourse away from political activity for better material working conditions to an individualized psychology-based call to improve behaviour and attitudes (both personal and institutional). … by embracing ‘work/life balance’, which reinscribes heteronormative “family life” […] [and] stands in binary gendered relation to an undesired identification with the work-obsessed male ‘nerd’ who has ‘no life’.”

    “This discourse plays in contradictory relation to the discourse of ambition, which implies that women need to improve themselves by being more attentive to public recognition and career advancement. Demands for the recognition of work/life balance issues are considered in the dominant male CS culture as a ‘personal choice’ to take a less committed (and therefore less materially compensated) role at work; this is also true for men in CS who want to take a more active role in parenting. The self-improvement discourse of ‘women in computing’ posits work/life balance as yet another performance indicator, suggesting that women should strive for high performance goals in both work and family life. This discourse seemed a contradictory and exhausting path for many of the women faculty members I interviewed, who balanced the demanding research performance that was expected of them at their universities with a heavy teaching and service workload and, in some cases, young children as well.”

    Sturman then spends some time analyzing the recommendations from CRA-W for faculty interested in increasing female participation in their classes, identifying how these texts focus “on the individualized promotion of ‘choice’ and ‘career satisfaction’ away from any analysis of a need for structural change.”

    Marketing the “Woman in Computer Science”

    Another theme of WIC events that Sturman observed was workshops and advice on how to market one’s self as a woman in computer science. Here, “an aestheticized anti-geek self-identity is encouraged as a marketable commodity for women seeking CS/IT careers”.

    In trying to combat the stereotype of computer scientists as male geeks, WIC culture presents women in computing as hyperfeminine. These idealized women are young, attractive, have “soft skills”, and also can balance work and life. Sturman spends some time analyzing the promotional material for Grace Hopper. These posters (at the time of analysis) always showed young pretty women, usually white. The posters never show the “old faces” of computing: white men and Asian women and men.

    The frequent suggestions that young women need “role models” serves to communicate to young women what kind of woman they should become in order to be a woman in computing. The graduate students that Sturman interviewed reported having to carefully present their identities, to try to further their careers.

    While at Grace Hopper, Sturman observed how much the celebration has become a place for tech companies to recruit women. Many of the women recruited to these tech companies are later paced into non-technical managerial positions.

    One session at Grace Hopper that Sturman attended was entitled “Embrace your duality as an Asian woman to lead” and its abstract adversities “We will brainstorm how to leverage our gender strength to excel, and to embrace our cultural advantage to lead!” At the session, Asian women were taught to treat their identity as a commodity. Universities are seeking to attract more “international students” and companies more “migrant Asian IT worker” – labels which serve to treat these women as outsiders, rather than North Americans.

    Networking at Grace Hopper

    Sturman’s research brought her to Grace Hopper. Her first impressions are clear: a focus on “the contested concept of numeric equality” in CS, and how the choice of naming the conference after a military scientist “provides a backdrop to the many intersecting contradictions in this milieu, where academic, corporate and military interests converge to produce the organizing texts forming the category of ‘women in computing’ as a target group for inclusion, marketing and co-optation.”

    She finds a paradox at the celebration: the “North American Second Wave liberal feminist belief in ‘universal sisterhood’” is present at the same time as neoliberal political and economic practices. Neoliberal political/economics refer to the decentralization of the State and the decollectivization of the workplace – or as their proponents would put it, making the workforce more “flexible”, and workers more “independent”.

    For the female students who attended, the main pull was networking with other women in the field, and with the large tech companies who recruit there. The older generation’s goals of universal sisterhood were not shared with the young: “there was little or no identification with networking as a method of establishing solidarity or group affiliation as ‘women’; most understood the practice as instrumental in the establishment of professional contacts, but little more”.

    Personally, I’ve had people recommend that I attend Grace Hopper so that I can “network with the Ol’ Gal’s Club” – the conference has established a network of high-achieving women to rival the Ol’ Boys Club of computer science, rather than dismantle the club-based system.

    Female Friendly Education?

    A particular session stood out for Sturman, titled “‘Female Friendly Education: Increasing Participation or Watering Down?’ provoked a heated response from audience members, many of whom were university students and professors. The topic unexpectedly made clear how the insertion of diverse women into a generalized female identity in ‘women in computing’ or ‘female friendly Computer Science’ is extremely problematic, constituting a unitary subject which also produces those it does not include as marginal. Among the panelists, Sue Rosser, a feminist scholar of science at Georgia Tech, had coined the term ‘female friendly science’ (1990) to signify a new approach to university science education which would take into account how science is gendered and would seek to redirect values and practices in scientific work to reflect the interests and experiences of women as well as men.”

    “Certainly, in the group of female graduate CS students I interviewed, the majority resisted the characteristics ascribed to them in a ‘female friendly’ Computer Science approach, characteristics which positioned them as gendered subjects, read as less competent and less able in relation to the dominant unmarked male subject of Computer Science. The panelists presented their questions to the audience in an attempt to get feedback about why ‘third wave’ (their term) feminists and younger CS women in general responded negatively to the term ‘female friendly…”

    “The practices of ‘female friendly’ science assemble a subject which is the Other, as the term ‘female friendly science’ itself acknowledges that the discipline of science limits the right of women to participate in its laws of discourse. Thus ‘female friendly science’ performs a reinscription of social exclusion. The existing forms of scientific discourse, or what the presenters described as masculinized STEM values, ‘an emphasis on truth, beauty and puzzles’, determine the limits by which femininized values of ‘time spent on community and social impact’ can be uttered as a ‘truth’ about women through the gender binary.”

    Retraditionalizing the Gender Binary

    Indeed, much of the rhetoric Sturman observed at Grace Hopper served to reinforce the gender binary. “Throughout the conference, there was a lot of talk about countering the geek image, supported by print and visual media; essentialized female identity appeared to shut out those who didn’t conform.”

    Much of the motivation for recruiting more women in to CS “reflected concern that young women will be ‘left behind’ because they are not pursuing Computer Science or IT as career paths; this is produced through a discourse of fear that America will be left behind in the face of global IT competition, particularly that coming from Asia. In the media representations at Grace Hopper, young women at the conference are heavily encouraged to be leaders in the field, icons for innovation, change, diversity, flexibility and collaboration; this supports an essentialized cataloging of women’s ‘inherent’ strengths in working with people and across difference.”

    “A series of keynote speakers modeled different aspects of computer science and information technology in the academy, in industry and in the U.S. Space program. This ‘intentional role modeling’ is seen by many promoters of women’s participation in IT as “a way to overcome the negative effects of stereotypes by increasing self-ratings and by inspiring and motivating achievement”. … Once again, this places young women in the dual role of being both strong and ‘at risk’ (Harris, 2001), strong in that their participation in IT work drives the national economy, but at risk of not living up to the expectations set by the role model, and potentially failing their own expectations for a high-paying and meaningful career. These intentional role models, as living motivational texts, organize work for those with already high self-expectations … and to push them to achieve further as selfactuating, self-(im)proving subjects. Not only is their own self-worth dependent on their achievement, but, they are told, so is the success of the nation: “Women’s lack of participation in IT has deep implications for our country’s preparedness, competitiveness, economic wellbeing, and quality of life” (NCWIT, n.d.).” (emphasis mine)

    The Merit vs. Equality Debate

    Noticeably, much of the policies and texts surrounding computer science education in Ontario place an emphasis on producing IT workers. “In much of the discussion around inclusion strategies in CS/IT education, the exchange often takes the form of a debate between those who point to the underrepresentation of women and targetted minority groups as not only inequitable but also potentially limiting to the development of future North American CS/IT workers, and those who argue against a ‘watering down’ of Computer Science as an academic discipline for the sake of filling enrolment targets.”

    Sturman is far from the first to observe the merit vs. equality debate in computer science. She presents some examples of texts representing these discourses. Since many others have covered this topic, I’ll be brief here.

    She draws a connection to the focus of numerical equality: that “dominant belief for practitioners is that getting more women into the field will change science; Schiebinger (2000) suggests that only feminist analysis alongside women’s greater participation within the field will bring that change about, as past history and current resistance to change indicates.”


    If it wasn’t clear by now, Sturman is not a positivist. Indeed, she argues that the hegemony of positivism in computer science is part of the problem: it leads to a focus on what is measurable and “objective” (ie. numeric equality) rather than on the “subjective” experiences and emotions of the women in CS.

    Positivism also falls prey to the grand narrative of cause and effect. She describes much of the research on women in CS: “The general purpose of these investigations seems to be to identify a cause-effect pathway and then to proceed with an equally linear solution. This strategy mirrors the methods of positivist science, and the reasoning is that a solution to this scientific/technical problem must use parallel scientific means … “The difficulty with this approach is that the subject of science is transparent to itself and tends to reproduce its own image iteratively. Thus the ‘problem’ of ‘women in computing’ bounces back to researchers without much in the way of new insights, let alone solutions. The ‘numbers game’ dominates, even as feminist proponents of gender equity in Science, Technology, Engineering and Mathematics (STEM) acknowledge its failure.” “

    Publishing practices also contribute to the “problem”: she talks to people who tried to publish “failed” interventions and were rejected. “Gender” is seen as a niche issue: “she was told that the journal had already published a lot of submissions on ‘gender’”

    It’s my opinion that the CS education scene has improved on these fronts since 2006 (there are good places like ICER and RESPECT that have been created, but they are not representative of all CS education). But women in computing groups today still seem uncomfortable with non-positivist research. I attended Can-CWiC earlier this year and noticed a huge divide between the female faculty (who wanted to hear about my research and were excited by it) and the female students (who were dismissive of me for not being a real “computer scientist”).

    A Final Quote from the Thesis

    One of Sturman’s faculty participants candidly noted that “…to keep in mind … is we’re the survivors. We’re the ones who could put up with all the crap, and we had enough, a strong enough sense of self, or our personalities are such that we fit in well enough with the dominant, mostly male environment that we were able to do fine. So we would be the last ones who’d be able to tell that the atmosphere is poor… in some sense we’re part of it, if that makes sense [Laughs]. We were selected for being able to put up with toxins, so we’re probably not the best ones to detect it, necessarily”

    My Own Thoughts

    My first reactions to the thesis were excitement that somebody has done this work – only to follow with disappointment that the work was never published, and never disseminated to the audience it would most benefit: computer scientists.

    Not only was it never disseminated, it’s written in a way that is really only accessible to theory-oriented sociologists. She describes almost nothing of her methods or analytic process. No primer is given on the many social theories that she uses.

    While poststructuralism doesn’t aim to verify hypotheses (like positivism) nor to explain the world (like constructivism), it does aim to provide new narratives to counteract problematic ones. An unpublished, inaccessible thesis does not help computer scientists rethink their problematic narratives and build new ones.

    Since Sturman did her research in 2005, some things have changed. Some of her critiques and observations have been independently made. For example, Sarah Nadav’s post that “The “Women in Tech” movement is full of victim blaming bullshit” recently went viral. And within the CS Ed community, I’ve spent some time at ICER critiquing women in CS initiatives for many of the same reasons, though based more on evidence from social psychology. I’ve also written on the generational differences between the women running Women in Computing events and the young women attending them (see here and here). I’m wishing I’d read her thesis three years ago!

    There are things that have been deconstructed since 2005 which Sturman didn’t pick up on. One is the pipeline discourse, which Sturman treats unproblematically. Another is her own, and her participants’ use of “females” as a noun, which is a personal pet peeve of mine.

    I found Sturman’s discussion of Unlocking the Clubhouse as simplistic: while she made the key observation that the initiatives had the political support to succeed, she views the curricular changes as the only ones. The folks I’ve talked to about CMU’s initiatives have indicated that it was the admissions changes that may have been the most significant.

    Since Sturman wrote her thesis, many scholars have critiqued how “women in STEM” is being presented. Marieke van den Brink, for example, has found many of the same discourses and paradoxes in the women in physics community. Padavic et al have pushed on the “work/life discourse” as problematic with interesting insights. Overall, the focus has been slowly shifting from trying to “fix” the women/girls to fixing the structure of science. Sturman’s thesis gives us more ammunition for arguing that the old way of promoting “women in computing” is problematic and in need of rethinking.

  • Computer Science as a Lake


    Imagine your CS department is a lake.
    The fauna of your lake are primarily fish and frogs. Normal lakes in your biome tend to have a food chain where about half of the predators are frogs and the other half are fish.

    Back in the 80s, the predators in your lake’s ecosystem used to be 40% frogs. But then the frogs started dying, or leaving, or not hatching, or whatever else caused their population to plummet. Now you’re at 25% frogs. And even though it looks like the fish are doing fantastically, there’s less of them than there should be.

    Somebody noticed that the nearly all the fish you see these days are either the white-coloured fish species, or the yellow-coloured fish species – the black fish and the red fish, for instance, are doing about as well as the frogs.

    But since the lake is now 75% fish, you’re really more concerned about the frogs. So, you put your efforts into releasing more frogs into the wild. You construct some frog-friendly spaces. You stop by and give them some canned flies to eat.

    The problem, though, is that the frogs are still gonna keep dying if the lake is polluted. The frogs are an indicator species – amphibians are more sensitive to environmental toxins. And that pollution is having the same effect on the red and black fish species, if you were paying more attention to them. The two fish species that are less affected happen to have some adaptation that is protecting them.

    If you start to remove the pollution from the lake it helps both the fish and the frogs. Sure, it has a larger impact on the frogs, but it also helps the fish. Some species and subspecies of fish were also getting disproportionately hurt by the environmental toxins too, and it’s good for them.

    If you want to fix the ecosystem, you have to fix the ecosystem for all the species involved. A culture polluted with the stereotype of computer scientists being asocial nerds in basements hurts the whole environment. It pushes women and other minorities away from going into the field, thinking it’s not where they can belong.

    For real change to happen in CS, we need to move past band-aids like Women in CS clubs and mentorship programmes for African-Americans. These initiatives have a role to play in the solution, but they can’t do it alone: We need to get rid of the pollution. Let’s work on cleaning up the lake for our whole ecosystem: it’s not about the frogs, it’s about the lake.

    ### How Do We Clean Up The Lake?

    I’m doing my PhD on diversity in CS. So far in my research, I have come to a hypothesis about diversity initiatives.

    Here’s my assertion: _A women in CS initiative is more likely to be successful in the long-term if that _particular_ initiative also helps other minorities (e.g. aboriginals, Blacks, Hispanics, people with disabilities, first-generation university students)._

    And by “particular initiative” I don’t mean “we started a mentorship programme for women in CS, somebody could make a mentorship programme for students with disabilities in CS” – I mean “we started a mentorship programme that all students can participate in”.

    Why is this important?

    First off, well-intentioned diversity initiatives can sadly be counterproductive:

    By calling out women for extra help, it reinforces that they are different, which reinforces their otherness. It can also trigger stereotype threat. Overtly feminine role models hurt girls’ interest in science – these women are seen as being unrealistic, and are not mentally categorized as scientists When teaching/recruiting, women speaking about CS are more likely to say the field is “hard” – and perceived difficulty pushes prospective students away from CS Diversity programmes cause people to take discrimination cases less seriously
    These types of initiatives are also prone to survivorship bias
    The empirical research we have on hand about what has long-term benefits shows us that the tricks that help women help everybody – and unintuitively, many of these are very small changes to make!
    Using assignments in first-year CS that demonstrate how CS contributes to society
    Value-affirming exercises to decrease stereotype threat Mentorship programmes for all students
    Exposure to non-traditional but non-counterstereotypic role models Ditching the nerd stereotype when depicting CS in media
    Shifting students from fixed mindsets to growth mindsets– for example, by talking about the struggles that great scientists have encountered and how they surpassed them – rather than focusing on their achievements Improving parental leave – having men and women share parenting more equally keeps more women in CS
    * Letting students know that it’s normal to worry you won’t fit in at first, but that it will go away with time
    Improving diversity for all underrepresented groups makes it easier for the other ones to show up – you’re removing pollution from the lake. Women are more likely to feel at home in a CS department that is multiracial – the more you pry CS away from being a white-and-Asian-boys club, the more you help all underrepresented groups join the community.

    We already know a lot about why women drop out or aren’t interested in CS. Let’s target those underlying reasons and fix them for all students, without framing them as women’s issues or ghettoizing women. It’s not about the frogs – it’s about the lake.
  • Why Are There More Women in CS in Other Cultures?

    The rates of female participation in CS – and STEM in general – vary wildly from culture to culture. In the US, women currently make up about 18% of undergraduate CS students [1], but over in Qatar, women make up about 70% of CS undergrads [2].

    Women in STEM are better represented in countries such as Turkey, Hungary, Portugal, and the Philippines. In these countries, women make up approximately 50% of STEM undergrads [3]. Indeed, well-developed countries like Canada, the US, and the UK have some of the lowest levels of female participation in STEM.

    So, what cultural factors lead to fewer or more women in STEM? Per the work of Barinaga, there are five factors [3]:

    1. Recently developed science capabilities, resulting in an unentrenched scientific community
    2. Perception of science as a low status career
    3. Class issues that overshadow gender issues
    4. Compulsory math and science education in secondary school
    5. Large social support for raising families

    New to Science

    While it’s a bit surprising that Portugal and Mexico have better levels of female participation in science despite these countries not having well established scientific scenes, the evidence is actually that they have these better levels because of the newness of their scientific communities [3]. In countries like the US and the UK, the scientific communties have entrenched cultures. So called “old boys networks” were built up before women were allowed into the labour market; science has been firmly established as a masculine occupation. Portugal, for instance, begin its scientific and technological establishments in the 20th century, when society was more open to female participation.

    It should be noted, however, that while countries like Portugal may have large numbers of women in science, few are making it to the top. Beatriz Ruivo, who studies female participation in Portugese science, has found that the
    glass ceiling there is partly due to the lack of a strong women’s movement in Portugal [3]. We see an interesting parallel in the history of computer science. In the early days of computer programming (30s-60s), most programmers and coders were women [4]. It was later when stereotypes of programmers being nerds developed – and IT companies began specifically hiring those who were like the nerds in order to make up for a labour shortage in the late 60s – that programming became highly masuclinized.

    Science As a Low-Status Occupation

    It is fairly established in the sociology literature that, across cultures, the lower the status and pay an occupation, the more likely it is that women will be found there [3]. And not only are women more socially encouraged to stay in low-status occupations, but some occupations are reinforced as having low status due to the large numbers of women – forming “occupational ghettos”.

    This was certainly the case in the early history of computer programming. Women were traditionally “computers” – those that did the hand computations, whereas men actually did the science [5]. When computers entered the mix,
    it was the men who were to decide what the computers should calculate, and women were left as the low status “coders” to carry out the low-level work [4].

    For countries with recently developed science communities, basic science is not highly connected to the production of goods and services. Science is hence seen only as an intellectual, cultural pursuit – not unlike how the humanities are regarded in the US and Canada. The humanities in North America are frequently (and unfortunately) derided as being “useless” – and have largely equal levels of women and men in modern day.

    In computer science, it has been noted that male students often select careers in CS for the money. As computer science has become known as a lucrative field, more men have been specifically drawn to the field – and driving out their female colleagues.

    A Matter of Privilege

    In India, southern Europe, and Latin America, the social hierarchy puts high class women above low class men [3]. In these countries, education is often limited to the upper classes, resulting in a very different environment in academia than in the general population.

    In North America, women from affluent communities, with parents in IT, were more likely to go into computer science themselves [6]. In short, the more privilege you have, the more likely you are to study CS – for instance, a White woman from a rich family and urban neighbourhood is more likely to have a job in STEM than than an Aboriginal man from a poor, rural family.

    For computer science, the digital divide plays in to class issues [6]. The low classes not only are less likely to receive higher education, but also less likely to be connected to modern computing. Without a connection to computers, one would expect fewer of them to study computer science.

    Compulsory Schooling – And Mindset

    Former Soviet countries have higher rates of female participation in science, and Barinaga attributes this partly to the requirement that all secondary school students take multiple science courses and mathematics [3]. As a result, girls “can’t ‘chicken out’” of science and don’t close doors on themselves before they reach university’’. The policy of teaching all science subjects, in particular, is beneficial – when students can choose one science out of a list (as is the case in many Canadian provinces), female participation in physics is reduced.

    The American approach of science being optional – and hence avoided by all but the gifted students – leads to a mentality to that you either have talent in science, or you don’t [3]. This fixed mindset approach to science has been consistently found detrimental both to individual success in science, as well as for minorities. In countries like Italy, where all sciences are mandatory, the communal mindset about science is a growth mindset: anybody can do it.

    Support For Families

    Forty percent of women who leave the workforce cite their husbands – and specifically, their husbands’ inability to pull their weight with housework and childcare – as their reason for leaving [8]. The United States was described by Barinaga’s international participants as “just a horrible place to try to raise a family and have a career’’. Without state-mandated parental leave, allowances for dads to stay home to look after children, and daycare, it is difficult for many women to manage both career and family.

    Contributing to the problem is the Protestant work ethic for men, leading men to focus only on work and leave everything else to their wives. In northern Europe, Canada, and the US, fathers spend less time looking after their families [3]. Female science participation is higher in countries where childcare is a shared responsibility: not just between father and mother, but also with the extended family, and society at large.

    This shared responsibility needs to be present in the workplace too; as one of Barinaga’s participants described: “if I missed a half-day of work [in the United States because] my kid had a temperature of 104, I was lectured on how this let down the [department]. In Israel there is 3 months paid maternity leave, day-care centers on every block, and if you don’t take off from work for your kid’s birthday party the department chairman will lecture you on how important these things are to kids and how he never missed one while his kids were little (Emphasis added).

    A Final Note

    Culture is a complex issue. None of the issues listed here can be a panacea for North American STEM. For example, even if we made CS obligatory in high school, it’s unlikely to have an effect for many racial minorities (Black/Hispanic Americans, Aboriginal Canadians, New Zealand Maori, etc), as these groups have low rates of high school completion [7]. By identifying these cross-cultural factors that promote women in STEM, we can better identify what factors (plural!) need addressing here in North America.

    [1] NCWIT By the Numbers. http://www.ncwit.org/resources/numbers
    [2] Guzdial. Women in CS in Qatar: It’s Complicated. http://computinged.wordpress.com/2010/05/03/women-in-cs-in-qatar-its-complicated/
    [3] Barinaga. “Surprises Across the Gender Divide”. Science 263, number 5152 (1994): 1486.
    [4] Ensmenger. “The Computer Boys Take Over”.
    [5] Rossiter. “Women scientists in America: Struggles and Strategies to 1940”, volume 1.
    [6] Ashcraft, Eger and Friend. “Girls in IT: The Facts”. http://www.ncwit.org/resources/girls-it-facts
    [7] Adams, Hazzan, Loftsson, and Young. “International Perspective of Women and Computer Science”. http://dl.acm.org/citation.cfm?id=611892.611897
    [8] Stone. “Opting Out? Why Women Really Quit Careers and Head Home”. University of California Press, 2007.

  • Generational differences of female scientists in academia

    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 First Generation

    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.

    The Second Generation

    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.

    Post-Etzkowitz et al: A Third Generation?

    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 alSteinpreis 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.

  • Why are there more women in some STEM fields than in others?

    Why is it that there are more women in biology than there are in computer science in North America? Women in the biomedical fields are now earning more than 50% of undergraduate degrees in the US [1].

    Biology, like computer science, was once stereotyped as masculine. Medicine continues to be stereotyped as masculine, especially fields such as surgery. Why has biology attracted so many more women than computer science?

    To answer this question, I’ll be synthesizing the findings of Cheryan’s “Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?” [2], Cohoon’s “Women in CS and Biology” [3], and Carter’s “Why students with an apparent aptitude for computer science don’t choose to major in computer science” [4].

    Between these three papers, four themes emerge for why women choose one STEM field over another:

    1. Exposure to the field
    2. Expected value of the major
    3. Lack of prejudice in the scientific culture
    4. Prospects of raising a family in that scientific culture


    The ultimate finding of Carter’s “Why students with an apparent aptitude for computer science don’t choose to major in computer science” is that students simply didn’t know what CS is, or had misconceptions of the field [4].

    Most undergraduates in North America never have to take any CS, and never saw any in high school. The big boon to biology enrollment is that biology is a course that pretty much everybody has to take in k-12. As we saw in comparing female representation in STEM between cultures, compulsory schooling plays a role in getting women into STEM.

    But high school science isn’t the only way to expose young men and women to science. Women are better represented in astronomy and the earth sciences than they are in computer science, and neither of those fields are well-represented in k-12. Exposure can come from museums; television programmes and other documentaries; popular science books, magazines and blogs; public lectures; and science camps. Computer science does comparatively little public outreach.

    Early exposure is also important. In Carter’s study, numerous students – disproportionately female – would only discover CS near the end of their degrees – too late to major or minor in the field. Multiple points of entry to CS majors, and multidisciplinary programmes, are hence recommended to increase female participation in CS [5].

    Exposure at an early age also is useful. Girls who are given hands-on exposure to computers at an early age are more likely to wind up in CS [6]. Girls whose mothers are confident around computers are more likely to be confident around computers [6]. Girls who come from academic families are more likely to wind up in CS [6].

    Finally, exposure is important for overcoming stereotypes about CS. Cheryan compared giving women descriptions of computer science as being a nerdy discipline – versus descriptions of computer science “not being like that” [7]. Women were statistically significantly more interested in computer science when given a non-stereotypic description of computer science.

    Expected Value

    Expectancy-value theory is one of the numerous theories out there used to model how undergraduates choose their majors. In a nutshell: undergrads are more likely to choose majors that they expect to align with their values and beliefs.

    Cheryan argues that women are choosing biology over CS because they see in as more fulfilling: there is the promise of intellectual challenge combined with the promise of benefiting society [2].

    But it’s not that simple – not all women have the same values, beliefs, and backgrounds. Margolis and Fisher found that women from racial minorities and international students who came to the US to study were motivated by the financial stability promised by a CS career [8]. And biology careers tend to appear more stable to undergraduates: biology faculty almost never turn over, whereas CS faculty will leave academia to go to industry. Maintaining a stable faculty in a department is good for gender representation [3].

    Actual Openness

    Another finding of Cohoon’s is that biology professors have better opinions of female students than CS professors do [3]. Biology professors also spend more time mentoring students than do CS professors [3].

    Biology continues to have issues with prejudice. Women are less likely to be hired than equally competent men [9], will be offered lower salaries [9], and their work is viewed less favourably [10]. But the evidence indicates that biology is still more open to women to female scientists than CS is. And as we saw in the cross-cultural comparison, an unentrenched scientific community is conducive for minorities to enter the community.

    Prospects of Raising a Family

    There’s little evidence that women consciously choose majors based on how friendly the major is with respect to raising a future family. But in a society where women are socialized from a young age to expect to be the primary caretakers of their future offspring, it is not surprising that women are deterred from fields that seem unfriendly to raising a future family.

    The process is more of an accumulation of red flags: The long hours in CS are only one red flag. As Carter found, CS is considered a volatile field without job security [4]. A lack of role models that are actively parenting adds to the notion of family-unfriendliness: they fail to provide evidence that women can have families and be in computer science. The relatability of role models is important: it is counterproductive in this regard to see female professors who have no families and are focused only on science [1].

    The stereotypes about computer scientists are another red flag: computer scientists are seen as unattractive, singularly focused on technology, and asocial. Male computer scientists hence are unattractive as potential partners – and there’s plenty of evidence that humans are subconsciously drawn towards careers that are more conducive to meeting potential partners [11].

    The sad evidence is that a fraction of white women are deterred from STEM because they do not want to be seen as unfeminine or intimidating to future partners [11]. Women who do go into STEM are more likely than non-STEM women to believe that men are unintimidated by their career choice, and they are more likely to have fathers, brothers and boyfriends that support this belief [11].

    Overall, this lines up with what we saw in the cross-cultural comparison: women are more likely to go into STEM in cultures where raising a family is viewed as a communal responsibility.

    [1] Ashcraft, Eger and Friend. “Girls in IT: The Facts”. http://www.ncwit.org/resources/girls-it-facts
    [2] Cheryan. “Understanding the Paradox in Math-Related Fields: Why Do Some Gender Gaps Remain While Others Do Not?” 10.1007/s11199-011-0060-z, Sex Roles 66 (3 2012): 184–190. issn: 0360-0025. http://dx.doi.org/10.1007/s11199-011-0060-z.
    [3] Cohoon. “Women in CS and biology.” SIGCSE Bull. (New York, NY, USA) 34, number 1 (February 2002): 82–86. issn: 0097-8418. doi:10.1145/563517.563370. http://doi.acm.org/10.1145/563517.563370.
    [4] Carter. “Why students with an apparent aptitude for computer science don’t choose to major in computer science.” SIGCSE Bull. (New York, NY, USA) 38, number 1 (March 2006): 27–31. issn: 0097-8418. doi:10.1145/1124706.1121352. http://doi.acm.org/10.1145/1124706.1121352.
    [5] Cohoon. “Recruiting and retaining women in undergraduate computing majors.” SIGCSE Bull. (New York, NY, USA) 34, number 2 (June 2002): 48–52. issn: 0097-8418. doi:10.1145/543812.543829. http://doi.acm.org/10.1145/543812.543829.
    [7] Cheryan, Plaut and Handron. “The Stereotypical Computer Scientist: Gendered Media Representations as a Barrier to Inclusion for Women.” Sex roles (2013): 1–14.
    [8] Margolis and Fisher. Unlocking the clubhouse: Women in computing. MIT press, 2003.
    [9] Moss-Racusin, Dovidio, Brescoll, Graham and Handelsman. “Science
    faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109, number 41 (2012): 16474–16479. doi:10.1073/pnas.1211286109. eprint: http://www.pnas.org/content/
    109/41/16474.full.pdf+html. http://www.pnas.org/content/109/41/16474.abstract.
    [10] Knobloch-Westerwick, Glynn and Huge. “The Matilda Effect in Science Communication: An Experiment on Gender Bias in Publication Quality Perceptions and Collaboration Interest.” Science Communication (2013).
    [11] Hawley. “Perceptions of male models of femininity related to career choice.” Journal of Counseling Psychology 19, number 4 (1972): 308.

  • Women in CS: A Historical Perspective


    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.

    Women as Computers: from the 1820s to the 1910s

    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].

    The expansion of “Women’s Work”: 1920s to 40s

    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]

    The Continual IT Labour Crisis: the 50s through 70s

    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.

    Academic CS: cyclical enrollments from the 80s to present

    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.


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    2. Luker, Kristin. Salsa dancing into the social sciences: Research in an age of info-glut. Harvard University Press, 2008.
    3. Ensmenger, Nathan. The computer boys take over: Computers, programmers, and the politics of technical expertise. MIT Press, 2010.
    4. Normalizing Female Computer Programmers in the ’60s
    5. Ashcraft, Catherine, Elizabeth Eger, and Michelle Friend. Girls in IT: The Facts, 2012.
    6. Slonim, Jacob, Sam Scully, and Michael McAllister. Outlook on Enrolments in Computer Science in Canadian Universities. Information / Communications Technology Council, 2008.7. Roberts, Eric S, Marina Kassianidou, and Lilly Irani. “Encouraging women in computer science.” ACM SIGCSE Bulletin 34, number 2 (2002): 84–88.
    7. Camp, Tracy, and D Gurer. “Women in computer science: where have we been and where are we going?” In Technology and Society, 1999. Women and Technology: Historical, Societal, and Professional Perspectives. Proceedings. 1999 International Symposium on, 242–244. IEEE, 1999.
    8. McGettrick, Andrew, Eric Roberts, Daniel D. Garcia, and Chris Stevenson. “Rediscovering the passion, beauty, joy and awe: making computing fun again.” In Proceedings of the 39th SIGCSE technical symposium on Computer science education, 217–218. SIGCSE ’08. Portland, OR, USA: ACM, 2008. isbn: 978-1-59593-799-5. doi:10.1145/1352135.1352213. http://doi.acm.org/10.1145/1352135.1352213.
    9. Cohoon, J. McGrath. “Women in CS and biology.” SIGCSE Bull. (New York, NY, USA) 34, number 1 (February 2002): 82–86. issn: 0097-8418. doi:10.1145/563517.563370. http://doi.acm.org/10.1145/563517.563370.
    10. McGettrick, Andrew, and Yan Timanovsky. “Digest of ACM educational activities.” ACM Inroads 3, number 2 (2012): 24–27.