Summary: April 20, 2017 Discussion

Corlett Wood presented “The benefits of diversity (in science).” Here is her summary:

In my talk, I explored how and why demographic diversity is beneficial in science and in non-science fields. I focused mainly on studies that explored the consequences of gender and racial/ethnic diversity.

The benefits of diversity fall into three broad categories. First, diversity begets diversity. Diverse role models contribute to the retention of underrepresented groups in science (Drury et al. 2011). Second, people from different backgrounds often ask different research questions and pursue different objectives. For example, medical studies that included female authors were more likely to examine health outcomes for both men and women (Nielsen et al. 2017). Combating gender bias in medical research is likely to mitigate some very real health risks: the majority of drugs withdrawn from the US market between 1997 and 2001 had greater health risks for women (US General Accounting Office 2001). Finally, diverse groups outperform homogenous groups. In the business sector, companies with diverse workforces or management outperform those that do not (Herring 2009, Kersley and O’Sullivan 2012). The performance benefits of diversity are evident in academic science as well. Papers with both male and female authors, or by ethnically diverse groups are published in higher-impact journals and cited more than those authored by homogenous groups (Campbell et al. 2013, Freeman and Huang 2014).

Widespread evidence that diversity improves group performance dispels two common misconceptions about science: that it is immune to cultural influences, and that scientific advances are driven by brilliant individuals rather than by great groups. The mechanisms underlying the benefits of diversity remain an active area of research, and there are at least three hypotheses to explain them. One is that diversity promotes critical thinking, because diverse groups are more likely to challenge ideas and subject them to scrutiny. Another is that demographic diversity is associated with functional diversity: diverse groups are more likely to approach problems with a complementary perspectives, approaches, and skills (Hong and Page 2004). A third is that diverse groups have better social dynamics, resulting in higher collective intelligence (Woolley et al. 2010).

The benefits of diversity outlined above are only a few of many, many arguments in favor of diversity. All disciplines should work to increase diversity because a lack of diversity in science is symptomatic of pervasive barriers facing underrepresented groups. Promoting diversity is an essential step towards justice and fairness in science and society; any other benefits are an added bonus.

Summary: March 21, 2017 Discussion

Philip Greenspoon presented “It STEMs from childhood: Gender stereotypes adopted by children as obstacles to eventual participation in STEM fields.” Here is his summary of the talk:

I began by discussing the mean gap between male and female students in high school math performance and how on average globally boys perform better by this measure (Machin and Pekkarinen 2008) – but that breaking the gap down by country reveals large differences across countries (Guiso et al. 2008), suggesting differences may be due to cultural or environmental effects. I then turned to variance ratios in math performance in which boys tend to have larger variances than girls in math performance, suggesting that the upper tail of math performance is predominated by boys. Breaking down the occurrence of exceptional math performance by country, however, reveals that countries differ in their representation of girls in the upper tail, with countries having higher GGI indices (a measure of gender equality) having more gender balance in exceptional math performance (Guiso et al. 2008, Kane and Mertz 2012).

I then turned to possible causes of unequal math performance among children, focusing on the role of stereotypes. I presented results from a paper (Bian et al. 2017) that showed that by age 6, girls are rating members of their own gender as less brilliant than boys are, and that by this age girls are showing less interest in activities which emphasize intelligence. Next, I turned to a study (Leslie et al. 2015) showing that how much a field is perceived as emphasizing brilliance negatively correlated with how many PhDs were awarded to women in that field in the US in 2011, and that this was true in the sciences as well as the arts.

Finally, I considered one of the psychological effects of internalized stereotypes namely stereotype threat, and presented data about how stereotype threat may compromise math performance for women doing math (Spencer et al. 1999), as well as how viewing differences in math performance as genetic as opposed to experiential may also compromise math performance in women (Dar-Nimrod and Heine 2006). Finally, I presented two studies on how our understanding of stereotype threat may be used to engineer interventions to alleviate stereotype threat – one in which students are encouraged to either view intelligence as malleable or to not attribute setbacks to their own intrinsic abilities (Good et al. 2003) and the other in which students read biographies of successful women prior to taking a math test (McIntyre et al. 2005).

Summary: Feb. 14, 2017 discussion

Locke Rowe presented “A Preliminary Look at Gender Effects in the EEB PhD Program”. Here’s a brief summary:

Locke discussed data on gender equity for EEB and across the university, focusing on four areas: (1) sex ratio in PhD programs; (2) whether gender alters completion rates (the percentage of students in a cohort who have completed their degrees by a particular year); (3) how leave-taking alters completion rates; (4) interaction effects between advisor and student gender. PhD programs are offered in four divisions (Humanities, Social Sciences, Physical Science, and Life Sciences).

  1. The graduate student populations are female biased in each division except the Physical Sciences, the fastest-growing division.
  1. Students in EEB show slightly higher completion rates and shorter median times to completion than the university-average. There is no evidence that gender alters the time to completion. The analysis lumps together students who enter with and without Masters degrees.
  1. Leaves of absence reduce the completion rate, but perhaps less so for parental vs. other types of leave. Female students have slightly but significantly lower completion rates overall, but females appear to have similar (or even higher) completion rates as male students when they do not take leaves of absence. Leaves seem to increase the time to completion, but does that mean that the leaves are too short to be useful or that there were issues that could not be addressed with a leave of absence? More data—e.g., exit surveys—are needed to understand what causes the correlation between leave-taking and reduced completion rates and what interventions could improve outcomes.
  1. Do female students aggregate in labs with female supervisors? Obtaining data is challenging and that limits the number of departments analyzed. We discussed preliminary trends and alternate ways of analyzing the data once more departments have been included.

Summary: Dec. 14, 2016 discussion

Brechann McGoey led a discussion on life history timing. She has kindly compiled detailed notes, and here is her summary:

I presented steps along the road to parenthood, and then parenting itself, and how each might present unique challenges to women in science. I then discussed the evidence about whether motherhood is a significant contributor to the leaky pipeline problem. We then discussed some possible solutions to the barriers faced by people through pregnancy and parenting. (Note that the focus is on having children, but that is not to imply that there are no other, equally important, family responsibilities for academics. The talk mostly focused on the challenges facing academics who can get pregnant, but all parents will bring their own perspective and face their own challenges based on their identities and life circumstances.)

The timing of competition, the length of training, the poor pay for long periods and winner take all setup mean that academia exacerbates social inequalities and expectations that make it harder for women to balance careers and parenthood. The collective result of all of our best choices given the biological, social and academia-related restrictions may be a pattern where women are underrepresented the faculty level.

Summary: Nov. 18, 2016 discussion

Megan Greischar led a discussion on some of the literature concerning implicit bias. Here is her summary:

I discussed different ways implicit bias is tested for in published literature. Williams & Ceci 2011 PNAS find no consistent pattern when comparing the percentage of PhDs held by females and the percentage of female hires for tenure track positions and infer that there is no systematic bias, and we discussed when these percentages could be misleading (e.g., when departments are growing at different rates). Thomas et al. 2015 PLoS ONE instead model demographic changes in faculty numbers (rather than percentages) and concluding that both the hiring and retention processes must be equitable to achieve parity.

Moss-Racusin et al. 2012 PNAS sent identical CVs for a lab manager positions and found that both male and female faculty ranked male applicants as more competent, hireable, and deserving of mentorship than female candidates. Faculty believed they were ranking real candidates who wished to obtain feedback on their applications. Using a different approach, van Dijk et al. 2014 Current Biology examined the probability of becoming a principal investigator, finding that being male significantly increased the odds of becoming a PI given the same publication record.

Williams & Ceci 2015 PNAS conclude that current faculty (male and female) show a 2:1 preference for hiring female candidates for tenure track positions. Their study differs from previous work in that the faculty knew they were judging hypothetical candidates (“Drs. X, Y and Z”). They were also unambiguously strong applications, which might be expected to reduce bias of research into racial bias (Ginther et al. 2011 Science). Williams & Ceci’s study design reduces bias from gendered language (and they do not examine the effect of gendered language in this study). Haynes & Sweedler 2015 Analytical Chemistry highlight these issues in their response to the Williams & Ceci study.

The subsequent discussion focused on how to detect and address bias. We explored a range of reasons why new hires might be perceived (or perceive themselves) to be less qualified than other candidates. The problem of perceived differences in quality may be especially severe for spousal hires and individuals hired as part of explicit efforts to increase diversity, even when those hires are clearly productive and influential scientists in their own right. We discussed how to deal with those perceived differences.