Between 2001 and 2008 there was a 79% decline in number of incoming undergraduate women interested in majoring in computer science
See https://www.heri.ucla.edu/monographs/50YearTrendsMonograph2016.pdf p. 293 ish
Trend from 2008 - 2015 grows from 0.3 to 1.7 (weighted in some way–I haven’t looked into exactly how), comparable to 1999 response. Still lower than peak in the 1980s.
Attribute to Hyde and Lynn 2008; Lubinski et al., 2001
The authors seem to imply that some deficit in mathematical and/or spatial abilities could be studied in isolation from individual (behavioral) and sociocultural influences.
Argue that statistics based on average biological variation don’t apply to STEM because STEM tends to be top 1% of performance.
Doesn’t explain cultural and social bias in assessments of the top 1%. Don’t show hard numbers for “top 1% of performance” in STEM fields.
Some variables are particularly sensitive: For example, in a meta-analysis, Voyer at al. (1995) reported that the effect size for mental rotation was dependent on the particular scoring method used. When the test was scored out of 20 (original method), sex differences were larger than when scored out of 40 (ds � 0.75–1.00 vs. 0.50–0.74, respectively); other scoring methods reduced the effect size further (d = 0.10– 0.19). Although such differences may be reasonable in terms of measurement issues, the issue becomes problematic if researchers collapse across studies with different scoring methods. In their meta-analysis, Linn and Petersen (1985) showed that the effect size for gender depended on the particular rotation test used, with the four most common tests yielding significant inconsistencies.
Some researchers attribute the gender gap in mathematics, in part, to negative stereotypes that are activated when gender is salient (Lewis, 2005). Female test takers who marked the box corresponding to their gender after completing the SAT Advanced Calculus test scored significantly higher than those who checked it before starting. Identifying gender after the AP examination rather than before was thus predicted to add nearly 4,700 women eligible to begin college with advanced credit for calculus (Danaher & Crandall, 2008), presumably because directing attention to gender at the start of the examination causes anxiety that impedes working memory and hence performance (Beilock et al., 2007; Schmader & Johns, 2003).
See for example, Kaufman, 2007. A modest argument: if we worked as “mental widget rotaters”, we may expect to see the observed lack of women for purely biological reasons.
Women perform similarly to men in terms of math grades and hold 57% of all professional occupations in the US, according to Ashcraft and Blithe, 2009 (p. 10). So unlikely that purely biological phenomenon precludes women from working.
Repeatedly, men have taken over whatever kind of work is considered more economically valuable, suggesting that gender workforce patterns are driven more by cultural and political forces rather than simple biological differences
From original Whitecraft and Williams chapter p. 224
Cultural stereotypes may be prohibitive: Cultural stereotypes as gatekeepers: increasing girls’ interest in computer science and engineering by diversifying stereotypes, Cheryan, Master, and Meltzoff, 2015
Young women more attracted to health-related careers because they place higher value on “people/society-oriented jobs”, from Eccles et al., 1999 in Sexism and stereotypes in modern society.
Margolis, Fisher, and Miller, 2000 report that female students tend to emphasize the importance of integrating computing with people through projects with more human appeal than do male counterparts.
Anecdotal evidence drawn from a small sample.
Margolis, Fisher, and Miller further report women show a high degree of enthusiasm at the start of their careers, but they may be pushed out by “pernicious ways in which male behavior and interest become standards for ‘the right fit’ and success.” The people they interviewed “who refused to conform to the image of image of the myopically focused ‘computer geek’ who ‘hacks for hacking’s sake’ might feel out of place.”
Again, anecdotal evidence drawn from a small sample.
Consider how this fits in with checking someone’s GitHub profile online for contributions to OSS.
My point is that staying up all night doing something is a sign of single-mindedness and possibly immaturity as well as a love for the subject. The girls may show their love for computers and computer science very differently. If you are looking for this type of obsessive behavior, then you are looking for a typically young, male behavior. While some girls will exhibit it, most won’t. But it doesn’t mean that they don’t love computer science!
Insight from a female computer science teacher from Margolis, Fisher, and Miller.
Based on information from the Bureau of Labor statistics. Adding more women to the workforce will simply provide more workers to fill available jobs.
The information-processing approach argues that individuals in diverse groups have access to other individuals with different backgrounds, networks, information, and skills. This added information should improve the group outcome even though it might create coordination problems for the group.
From Mannix and Neale 2005. Would be nice to see if people have tested this hypothesis. Maybe The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies New Edition by Page 2007
Mannix and Neale show tenure diversity has a negative effect on performance and mixed, proportion-dependent effects when race was considered. Kochan and colleagues, 2003: gender effect either neutral or positive on performance; race tended to be negative. Confounding factor was that contextual conditions (such as competition among teams) tended to affect these measures of race’s impact on performance.
2006 Catalyst report by Carter and Joy “found higher average financial performance for companies with higher representation of female board members.”
So, we lack clear measures to point at gender diversity’s impact, at least with regard to performance.
Kochan sums it up, saying, “The diversity industry is built on sand. The business case rhetoric for diversity is simply naïve and overdone. There are no strong positive or negative effects of gender or racial diversity on business performance.” However, “there is a strong social case for why we should be promoting diversity in all our organizations and over time as the labor market becomes more diverse, organizations will absolutely need to build the capabilities to stay effective.”
Are gender and racial diversity the only meaningful categories of diversity to consider?
Educational interventions at post-secondary level. Is this really all we can do as a society?
Gender disparities in tech fields exist across national boundaries.
Given potential benefits to women and society, it seems advisable to consider steps that may encourage women to enter the fields of Information technology, Computer Science, and Computer Engineering. Cultural, curricular, and confidence-oriented interventions have been suggested by various authors [Margolis et al. 2000]; [AAUW 2000]; [McGrath Cohoon and Aspray 2006], and should continually be assessed regarding whether they are effective in the first place, whether they advance or hinder female participation in the field of computer science, and whether these changes in fact enhance the field. The ultimate goal should be the quality, effectiveness and advancement of the CS profession, regardless of whether this means that the futuristic view of CS is largely male, largely female, or somewhat more gender balanced.