Conclusions and Recommendations

Conclusions and Recommendations

DOI: 10.4018/978-1-4666-5860-8.ch010
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The focus of this chapter is recommendations. Overall and individual chapter conclusions are summarized; then 3 overarching and 35 detailed recommendations are presented of relevance to all stakeholders from governments to theorists to individuals. The primary recommendation is simple: Stop.
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No sensible decision can be made any longer without taking into account not only the world as it is, but the world as it will be. – Isaac Asimov (Notable Quotes, 2013)



For decades, we have been worried about how few women there are in IT and other technical fields. Based at least partly on the historical bias against women having any career at all, we have generated theories and interventions relating to surmised barriers that keep women out of IT or drive them from it after they enter it. We have achieved nothing.

But instead of questioning our assumptions, we have held them inviolable and linked them to increasingly subtle and insidious barriers. It gives the impression of women as a herd of frightened deer, ready to run at the mere sniff of a man.

Yet that is not how women in IT or girls interested in STEM see themselves: they see themselves as interested, competent and above all as people who regard obstacles as a challenge to be met and overcome.

That there are statistical differences between men and women is considered controversial in some contexts, but in fact is widely accepted implicitly or explicitly. “It is widely acknowledged that women bring a different mix of skills to the workplace – the area of contention seems to be whether these differences are good or bad” (Cave, 2013). For example, “Both women and men believed that women affected the organizational culture in a positive manner: by encouraging more collaboration, more participative decision-making and hence more collegial workplaces” (Soe & Yakura, 2008). Similarly, “A wealth of research in the past decade shows that diversity improves problem-solving, productivity, innovation, and ultimately, the bottom line” (Ashcraft, Eger & Friend, 2012).

Indeed, that there are relevant differences between men and women is implicit in many explanations in the literature for the shortfall of women – by the fact that they affect girls specifically. For example, Ashcraft et al.’s (2012) argument that “Computing is often taught in the abstract... This lack of relevancy is troubling because making relevant connections is particularly important for increasing girls’ interest in computing courses and careers,” and Miliszewska & Moore’s (2011) conclusion that we need “an alternative approach – a re-conceptualization of ICT into an environment that women would naturally embrace.”

At school, it is considered that boys and girls have different learning styles and boys are more assertive in the classroom (Herrick, 2009), and “While boys and girls have very similar levels of academic ability, boys are substantially more likely than girls to choose more prestigious profiles” (Buser, Niederle & Oosterbeek, 2012). It is interesting that this difference extends into adulthood: Cohen (2013) relates that generally women do not appear motivated by the prestige and self gratification of presentation, so when approaching female speakers TED adjust their requests to reflect the social impact of the presentation.

Research into gender differences including how wide they are and to the degree to which they are innate or learned has produced varying results. The results are complicated by human complexity itself: measurements can be affected not only by primary causes but by such self-referential factors as expectations. Nevertheless it is clear that there are varied and significant innate average differences between males and females (Yoshida, 2013). Indeed, recent research indicates significant differences in the neural wiring of male and female brains (Lewis & LiveScience, 2013), though what it means remains a matter of debate (Jarrett, 2013).

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