{ sociology }

  • What's different between female STEM workers and those in other professions?

    Many studies of women in STEM use men as a referent group to women: how do women compare to men in CS with regard to retention, attitudes, discrimination, etc? While there’s certainly benefit to using men as a referent group (and it’s far, far better than no referent group at all), there’s a threat to validity that we tend to overlook when studying women in CS: how much of what we see is an artifact of CS culture versus that of our wider society? **
    Triangulation using different referent groups is a good way to get around this issue. I’ve talked before about differences between women in CS vs. other STEM fields, differences between women in CS between different cultures, and differences over time/generations. But in every one of these posts, I’ve really only looked at scientists.

    Glass et al’s “What’s So Special about STEM? A Comparison of Women’s Retention in STEM and Professional Occupations“ addresses another angle: what’s different for women in STEM vs. women in other professional occupations? After all, women are more likely than men to leave other professional occupations such as business, medicine and law [1]. And in all these fields, substantial problems remain at the top: women may make up a substantial proportion of workers, but a tiny minority of those running the show.

    The Glass et al Paper

    To make the comparison of STEM women and non-STEM women, the Glass et al paper uses longitudinal data from the National Longitudinal Survey of Youth 1979. The longitudinal approach is a strength of the paper. A weakness, however, is that the women participating are a single generational cohort who entered the workforce in the late 80s/90s: “second generation” per my previous post.

    Overall, Glass et al found that women in STEM jobs had more in common with women in non-STEM professional jobs – and that “few differences in job characteristics emerge” overall. This is a rather important finding – it means that if we work carefully, we can often generalize findings about women in the general workforce to women in the STEM workforce.

    I say “carefully” since there were a few differences that they found. Here’s what’s unique to STEM women:

    1. Women who are married to fellow STEM workers are nearly 100% more likely to stay in their STEM jobs than women married to non-STEM workers.2. A higher education does not increase a STEM woman’s likelihood of staying in a STEM career. In other occupations, such as medicine or law, the more advanced degrees a woman has, the more likely she’ll stay in the field. Glass et al attribute to this to the type of work done by those with MSc/PhDs: the more education you need to do the job, the more likely it’ll be isolating and in a “noxious” work environment for women.
    2. Unlike non-STEM women who leave their jobs to stay at home, when STEM women leave their jobs, they overwhelmingly do so to fill non-STEM jobs, rather than to stay at home permanently. Switching to management explains almost a quarter of these job departures.Those three differences aside, everything they looked at turned out to be the same for both STEM women and non-STEM women.

    Similarities between female STEM and non-STEM workers

    For both women in STEM jobs and women in professional non-STEM jobs, the following things are positively correlated with the retention of women in the workplace: Higher pay, job commitment, higher reported job satisfaction, longer time working in that career, and the presence of parental leave.

    Sociologists have documented the “Work-Family Narrative“ – the cultural narrative that women leave (or struggle with) their jobs because they can’t balance work and family. They’ve similarly documented that the majority of workplace interventions to improve the status of women focus on this narrative.

    Yet, what Glass et al found is that primary propellant of women out of the workforce – both STEM and non-STEM – is not childcare. Nor is it lack of confidence or lack of training – or lack of “leaning in”.

    The primary propellants are dissatisfaction with pay and promotion prospects. There’s a ton of sociology papers out there finding similar results. Childcare might be the catalyst for acting on that dissatisfaction, but it’s not the underlying cause.

    This dissatisfaction is linked to a number of sources of inequality, such as being left out of the “boys networks”, subconscious biases against women, open prejudice about the competence of women, and sexual harassment. Correspondence studies of women in STEM and other professional domains have consistently found that women are less likely to be thought worth of a promotion as an equally qualified man, less worthy of a higher salary, and less likable overall. And there’s evidence that men in our society are promoted based on potential – while women are promoted based on past accomplishments. This sort of unintentional, de facto discrimination is not unique to STEM.

    The Work-Family Narrative as a Social Defense

    The paper I linked to about the “Work-Family Narrative” – by Padravic and Ely – presents a rather compelling argument that the reason that people focus on this narrative is because it is an unconscious social defense. The Work-Family Narrative gives a way of thinking about the problems facing women in the professional workplace that doesn’t involve coming to terms with discrimination and systematic problems in the workforce.

    Padravic and Ely also argue that this narrative similarly allows people to keep their cultural stereotypes in tact: women are the caregivers, men are the workers – and so women have a hard time in the workforce because they must balance their position as caregiver. I’ve noted before that our brains are wired to keep cultural stereotypes in tact.

    Discrimination is an ugly thing to talk about. I don’t blame people for shying away from it. But it needs to be tackled to change the numbers of women in the workforce – whether it be STEM or other fields. And it’s important to compare STEM to the rest of society – we need to know what’s a STEM problem and what’s even more systematic.

  • 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.

  • A brief introduction to social theory


    Theories from psychology enjoy a fair bit of use in computer science education, but education is not merely a cognitive process: it’s also a social one.

    I’ve found it useful to learn about social theory as a CS education graduate student, and I thought I’d share a quick introduction to social theory that I initially wrote for my research proposal to my thesis committee this fall.

    Classical Social Theory

    Classically, sociology has had four major schools of thought, each of which goes by various names and is associated with one of the four “founders” of sociology:

    1. Auguste Comte (1798-1857) coined the term “positive philosophy”, now better known as positivism. Comte’s sociology was inspired by the French Revolution: sociology was envisioned as a means to produce the perfect society. (Indeed, Comte was incensed that the lower classes wouldn’t simply accept their “place” in society.)
      In Comte’s world, one would test out different ideas for how to run a society, and find the optimal approach. While Comte himself argued for holism, (post)positivism has since come to be associated with reductionism.

    2. The sociology of Max Weber (1864-1920) contrasts with Comte’s: Weber was a proponent of anti-positivism (also known as constructivism or interpretivism). Weber saw verstehen (understanding) as the goal of research, rather than hypothesis verification. Weber theorized upon social stratification; he also wrote about closure (how groups draw the boundaries and construct identities, and compete with out-group members for scarce resources.)

      Weber’s sociology put an emphasis on ideology. Capitalism, for example, was the result of ideological conditions unique to Northern Europe; capitalism has only succeeded where these ideological conditions hold.

    3. Emile Durkheim (1858-1917) built on Comte’s positivism, setting forth structural functionalism. In structural functionalism, a society is viewed like a biological cell: different parts of a society are likened organelles. Durkheim’s sociology looks at how the parts work together to comprise the whole. It is also holistic – much of how systems thinking was used in the social sciences built on Durkheimian notions of society.

    4. Finally, Karl Marx (1818-1883) provided an approach which contrasts with Durkheim’s: instead of seeing harmony, it emphasizes the role of class conflict in society and the historical-economic basis thereof. Marxist sociology has also been known as conflict theory, though the historical-economic basis is not the only way one could study conflict. For example, Weberians see conflict rooted in ideology, rather than in a clash over resources.

    20th Century Social Theory

    While classical theory is often referred to in terms of thinkers (Marx, Weber, etc), the more modern movements tend to be known more by schools of thought. Some of the major ones would be:

    1. Neo-Marxism refers to the 20th century updates of Marxist theory, which has pulled in Weberian and poststructuralist work on status and power. Antonio Gramsci is a well-known neo-Marxist, who was curious about the question of why the revolution Marx had predicted never seemed to come about. Gramsci is most famous for his theory of cultural hegemony.

      Critical theory is also based on Marxist thought, and is often conflated with it. Critical theory emphasizes praxis, the combination of theory and practice. Critical theory is most associated with the Frankfurt School, including names such as Theodor Adorno and Jurgen Habermas.

    2. Interactionism assumes that all social processes are the result of human interaction. It emerged in the early 20th century. Interactionaists focus their studies on the interactions between individuals. As a result, interactionists do not `see’ the effects of physical environment – or even solitary thought/work. They also reject quantitative data in favour of qualitative approaches: grounded theory and ethnomethodology were both developed by interactionists. The notion of social interaction as a performance was first developed in interactionist thought; poststructuralists have since refined it. While there’s no single name associated with this perspective, some associated names include George Herbert Mead, Erving Goffman, and Dorothy Smith.
      Sociologists also look at social systems at different levels: the macro level looks at entire societies, nations, etc; the meso level looks at organizations, institutions, etc; the micro level looks at individuals. While classical sociology was generally macro, interactionism focuses on the micro level.

    3. Structuralism might be seen as a macro-focused backlash against interactionism’s focus on the micro. Structuralism sees social processes as stemming from larger, overarching structures, and also emerged in the early 20th century. Structuralists see society as being governed by these structures in a somewhat analogous fashion to how physicists may see the universe as being governed by laws of nature. A criticism of structuralism is that it sees these structures as fixed; in contrast a Marxist would focus on historical change. Some structuralists include Claude Levi-Strauss, Ferdinand de Saussure, and Jean Piaget.

    4. Poststructuralism (more or less interchangeable with “postmodernism”) is not a particularly coherent school of thought. This is not altogether surprising as the key poststructuralists both reject the label and the very notion that there is such a real thing as poststructuralism. Poststructuralists reject the idea of “objective” knowledge: since the study of sociology is done by humans who are biased by history and culture, they argue that any study of a social phenomenon must be combined with how the study of that social phenomenon was produced. For example, a poststructuralist would not take a concept like `gender’ as a given, but problematize the concept. Poststructuralism evolved out of structuralism in the mid 20th century. Some major poststructuralists include Michel Foucault, Jacques Derrida, and Judith Butler.
  • A quick and dirty introduction to Bourdieu for systems thinkers


    I’ve been on a Bourdieu kick for the course I’m currently taking on social theory (LHA 1803Y: Theory in Higher Education), and since Steve Easterbrook mentioned he wasn’t familiar with Bourdieu, I figured I’d write a quick and dirty introduction to Bourdieu’s social theories. Steve’s a systems thinker so this is written for such an audience.

    In systems thinking we like to think of people as existing in many (overlapping) social systems (because, after all, pretty much everything to a systems thinker is a system.) These social systems can be things like school, work, a professional community, or even your favourite internet community.

    Bourdieu would call those systems fields. (Specifically, a field is a system of social positions, with internal structure.) In his terminology, the rules determining the system/fields are known as nomos. (Fields are not the same as class, which I’ll get to later.) When people in fields ‘play by the rules’ of the system, and invest in it, he calls this illusio.

    If you’re wondering if he also has paradigms in his systems, the answer is yes! He calls them doxa, the concepts and ideas which go without saying as it comes without saying – “the universe of possible discourse”.


    As an individual, I interact with numerous fields. There are two things that matter about me in how I interact with these fields: my capital, and my habitus.

    Bourdieu distinguishes numerous forms of capital:

    • economic capital, how many financial assets I have
    • social capital, who I know, my social networks, what I can get out them, etc.
    • cultural capital, the knowledge, skills, advantages and education that I have – along with the cultural ‘know-how’ of how to navigate particular social situations
    • symbolic capital, the resources available to me on the basis of honour, prestige, recognition, or my other forms of capitalA thing worth noting about capital is that its value is context-dependent. For example, my knowledge of Star Trek trivia (which is cultural capital), has use in nerdy fields like computer science, but less so at gatherings of my extended family. Similarly, my Canadian money is of lesser value outside of Canada, and even less value were I to visit a society with a bartering or gift-giving economy.

    As for habitus, the description on Wikipedia wraps it up nicely: “the habitus could be understood as a structure of the mind characterized by a set of acquired schemata, sensibilities, dispositions and taste”. Your habitus is developed in part through socialization. Bourdieu conceived of habitus as a way to study the interaction area between individual and society – in software engineering terms you might think of your habitus as being the coupler between an individual and society.

    Every individual has a habitus – but likeminded individuals together can have group habitus. For example, a class habitus would refer to sensibilities, dispositions, tastes and ways of thinking about the world that are common to a social class.


    Unlike previous social theorists like Weber or Marx, Bourdieu’s view of class is not based on just economic capital, but instead on all capital (social, cultural, economic, symbolic). For me this is an appeal of Bourdieu: it explains why a ‘blue collar’ tradesman making the same amount of money as a ‘white collar’ adjunct are not really the same class.

    Much of Bourdieu’s work looks at the reproduction of social inequality. He identifies feedback loops (though not by name) of what keeps the classes separate – and cultural capital plays a major role. How much cultural capital a person has, and to what social standing they are born, are determine their social mobility.

    Those with a large amount of cultural capital in society are able to determine taste in society – such as what is low-brow vs. high brow (and everything in between). And people judge other people based on taste – does somebody ‘fit’ into a particular field? When people don’t fit in to a particular field, symbolic violence is used to keep them out or make them feel uncomfortable. Symbolic violence (also known as symbolic power) comprises things like implicit biases, microaggressions, de facto discrimination, and all that other lovely stuff that’s used to “keep them in their place”.

    Symbolic violence allows for the reproduction of social divisions. A kid growing up in a working class family is going to have less access to means of accumulating cultural capital, is more likely to be affected by class dispositions to not value education as highly, and is more likely to say and do things when interacting with intellectuals that make them stick out like a sore thumb. Indeed, Bourdieu focuses extensively on the little things that stop people (or allow people) to accumulate capital – what they wear, the things they say, the hobbies they have – for him it’s all about the gain on the feedback loops here. The word ‘accumulation’ tends to come up a lot.


    The stories women have of not feeling like they belong in tech/CS are the type of thing very amenable to a Bourdieusian analysis. Indeed, his early work focused on how the French university system amplified social inequalities (both in terms of gender and of class – and the intersection of the two, well before the term _intersectionality _took off).

    His work has been used quite a bit by sociologists to look at the reproduction of social inequalities – for example, a recent study used Bourdieu to examine how low-income minority ethnic groups feel alienated when they go to science museums – and why they don’t really go to them in the first place (hint: habitus plays a role – they have not been socially conditioned to see museums as a thing worth going to).

    In many ways Bourdieu was a systems thinker – he thought of feedback loops, systems-within-systems, and systems-level behaviours, structures, rules and paradigms – but did not have the systems thinking vocabulary available to him. While he was active at the same time as the Systems Thinkers in the English-speaking world, few Systems Thinking based books have been translated into French – and most of the translations happened after Bourdieu died in 2002 (for example, Limits to Growth wasn’t translated into French until 2012!)


  • Subtyping, Subgrouping, and Stereotype Change

    There’s been a fair bit of research finding that negative stereotypes are part of what deters women and racial minorities from computer science and STEM in general (e.g. [1]). These stereotypes make it harder for women and minorities to personally identify with computer science, and amplify some of the biases that they face in CS. So for this post, I’ll be going over observed phenomena in social psychology and sociology that pertain to stereotype change.


    Stereotypes are really hard to change. They’re reinforced from many sources (media, individuals, groups, etc). But even more than that, stereotypes are schema: they are how we mentally organize information about social groups, and how we can determine whether we are “in” or “out” of a group. Schema allow us to process information effortlessly, and are pretty deeply ingrained once they’re there.

    The human brain is not very good at changing schema. When we see evidence that contradicts our schema, our brains will do all sorts of mental gymnastics to avoid confronting or changing the incorrect schema. Most frequently, we forget that we saw it all. Sometimes our misconceptions even get stronger [2].

    This happens with stereotypes. Betz and Sekaquaptewa did a study where they showed role models to young girls, to try to motivate the girls’ interest in STEM [3]. Role models were either gender-neutral, or feminine. The result? Gender-neutral role models boosted interest – and feminine (counterstereotypic) role models actually reduced girls’ interest in science. To these girls, the feminine scientist – a stereotype violator – is aberrant.

    Stereotype violators are not viewed favourably by others. Indeed, in laboratory settings, people go out of their way to punish stereotype violators [4]. Stereotype violators are seen as less likable, and less competent. Not surprisingly, women in science are rated as less likable and less competent than otherwise identical men [5, 6].


    So, let’s say instead of being exposed to just one woman scientist, you are exposed to a bunch of them. Regularly. That will change your schema, right? Nope.

    The human brain does a thing that social psychologists call subtyping. Instead of changing your mental model of what a scientist is (white male), you instead create a new category: the woman scientist [7].

    And the evidence is that this is what happens to female scientists, and to female engineers [3]. Furthermore, the stereotype of the woman scientist is of an unfeminine woman. The unfeminine label in of itself is costly: these women are seen as less likable, less attractive, less competent, and less confident [3].

    Perceived Variability

    So how can we change stereotypes, then? It turns out a thing called “perceived variability” is key: it’s how much variation we perceive in an out-group [7]. “Out-group” here refers to any group that a person does not identify with; an “in-group” is one that that person identifies with. Humans systematically underestimate the variability within an out-group, particularly in comparison to the variability within the in-group (e.g. men see women as more homogenous than they are; whites see aboriginals as more homogenous; etc).

    This is known as the Out-group homogeneity effect. We mentally exaggerate the stereotypical qualities of outgroups (and outgroup members), and ignore the counterstereotypical qualities.

    We stop paying attention to stereotypes when we perceive greater variability in the group that’s been stereotyped [7]. For example, it’s a lot harder to think about aboriginals in terms of generalizations and stereotypes when you’re used to thinking about the differences between Inuit, Metis and First Nations, and differences between the Haida, Salish, Blackfoot, Anishinaabe, Innu, Mi’kmaq, Dene, etc.


    So how can we increase the perceived variability of an outgroup? Subgrouping refers to the process in which both people members are brought together around common goals or interests, and can include both in-group and out-group members. For example, creating a study group in a computer science class in which both women and men are represented  – or joining a robotics club which has a mix of white, Asian, black, and hispanic students.

    Subgrouping “allows for a more varied cognitive representations of group members” [7] – it leads you to start seeing the members of your subgroup around their membership in your common subgroup – rather than their membership in any in-group or out-group. Richards and Hewstone have a very nice literature review about subgrouping and subtyping, showing how dozens of studies have consistently found that subgrouping leads to increased perceived group variability, and stereotype change.

    The Contact Hypothesis in sociology gets at subgrouping: the observed effect that being familiar with a member of an outgroup (eg. homosexuals) increases your acceptance of the outgroup. Having a friend, classmate or family member who is queer means you a share with a subgroup with them (friend group, class, family, etc).

    For subgroups to form effectively, they need to have meaningful cohesion to those in the subgroup. One study by Park et al that is described by Richards and Hewstone found that they could not form a subgroup around all engineering students: “[they] were all hardworking and bright, but in very different ways. Some were motivated only by money, some by parental expectations, and some by larger environmental goals.” Instead, there subgroups of engineering students formed, around those three motivators. [7]

    Similarly, Park et al found that they could not form subgroups around continuous variables (high/moderate/high) or arbitrary bases [7]. And other studies in the Richards and Hewstone review found that trying to form subgroups around having minority status (e.g. clubs for women in STEM, study groups for black students) either did not change stereotypes about their group, or intensified them [7].

    Indeed, I wouldn’t be surprised that part of why instructional techniques such as Peer Instruction disproportionately helps female CS/physics students is because they encourage subgrouping. When you have your whole class together, collaborating in small groups for class activities, you’re having them bond as classmates – rather than as members of in-groups or out-groups.


    This is one reason I’m always a bit iffy about Women in CS/Science clubs: they don’t promote stereotype change, but instead promote subtyping. Instead of changing the notion of what a computer scientist is, they reinforce the subcategory of woman computer scientist.

    But stereotypes aren’t the only thing that affect minorities. Part of why Women in CS clubs are so popular is that they provide a sense of community to these women. This is really important when you’re a minority member! The sense of isolation that many women experience in STEM is why many of them leave.

    And, as always, is evidence that girls only schooling can be good for encouraging young girls’ interest in math and science [8]. It’s somewhat of a tragedy of the commons problem: putting all the women together in a club helps those individual women cope with a culture in which they are negatively stereotyped – but it doesn’t change the actual stereotype.

    [1] Cheryan, Sapna, et al. “The Stereotypical Computer Scientist: Gendered Media Representations as a Barrier to Inclusion for Women.” Sex roles 69.1-2 (2013): 58-71.
    [2] McRaney. The Backfire Effect. http://youarenotsosmart.com/2011/06/10/the-backfire-effect/
    [3] Betz, Diana E., and Denise Sekaquaptewa. “My fair physicist? Feminine math and science role models demotivate young girls.” Social Psychological and Personality Science 3.6 (2012): 738-746.
    [4] Rudman, Laurie A., and Kimberly Fairchild. “Reactions to counterstereotypic behavior: the role of backlash in cultural stereotype maintenance.” Journal of personality and social psychology 87.2 (2004): 157.
    [5] Steinpreis, Rhea E., Katie A. Anders, and Dawn Ritzke. “The impact of gender on the review of the curricula vitae of job applicants and tenure candidates: A national empirical study.” Sex roles 41.7-8 (1999): 509-528.
    [6] Moss-Racusin, Corinne A., et al. “Science faculty’s subtle gender biases favor male students.” Proceedings of the National Academy of Sciences 109.41 (2012): 16474-16479.
    [7] Richards, Zoë, and Miles Hewstone. “Subtyping and subgrouping: Processes for the prevention and promotion of stereotype change.” Personality and Social Psychology Review 5.1 (2001): 52-73.
    [8] Barinaga, Marcia. “Surprises across the cultural divide.” Science 263.5152 (1994): 1468-1470.

  • 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.