Kate Brown led a session on Safe Field Work: Risks, proactive strategies, and responding to harassment in the field. Attached is her summary!

Teaching is hard! Teaching online during a crisis is extra hard, but it is precisely during precarious times such as this that it is important than ever to ensure our classroom is inclusive and every student is given the same opportunity to achieve their goals. Here, Amber Gigi Hoi shares some of the things she learned through the wisdom of the Academic Twitter community, as well as some personal reflections as she bumbles her way through getting her third-year biostats course running online.

(This is a transcript of Amber’s workshop for EEB’s TA training day, September 16, 2020)

Megan Frederickson led a discussion on the gender gap in faculty salaries. The gender pay gap is an ongoing problem in many workplaces, and universities are no exception. It persists despite legislation and other efforts aimed at closing the gap, and even though the principle of “equal pay for equal work” is fairly uncontroversial.

With the aim of improving awareness of this issue among Canadian academics, Megan wrote an article on the gender pay gap at Canadian universities that was published by The Conversation and re-published by Macleans.

Briefly, Megan analyzed publicly available data from several sources to measure the gender pay gap at Canadian universities and explore its causes:

1) She used Statistics Canada data on faculty salaries to get a Canada-wide perspective on the gender pay gap. This revealed that the gap between male and female faculty salaries at the University of Toronto is $23,275 a year, or 14%. Also, Canada’s research-intensive universities, known as the U15, tend to have larger gender pay gaps than other institutions. Megan made her code publicly available at: https://github.com/drfreder/statscan-gender

2) Megan then used salary data for Ontario professors available through the Ontario public sector salary disclosure, also known as the sunshine list. These data are interesting because they provide salary information for over 14,000 individual professors, going back in some cases over 20 years. Importantly, because there is a minimum salary of $100K to be included on the sunshine list, the sunshine list data underestimates the gender gap by about $3K a year. Nonetheless, the gender pay gap measured from the Ontario sunshine list is highly correlated with the gender pay gap measured from Statistics Canada data. Again, Megan has made most of her code publicly available (with the rest forthcoming) at: https://github.com/drfreder/Ontario-sunshine

The sunshine list salary data allowed Megan to explore four factors that may contribute to the gender pay gap, specifically gender differences in:

a. Seniority: The median male professor has been on the sunshine list only one year longer than the median female professor. This accounts for about $5400 a year, or less than half, of the gender pay gap.

b. Merit: Megan used publicly available data on operating grants made to professors through the NSERC Discovery Grant and SSHRC Insight Grant programs as a measure of “merit” or “performance.” The gender gap in salary is almost unchanged after adjusting for grant size, suggesting that differences in performance do not drive the pay gap.

c. Area of study: The gender pay gap is actually slightly larger among SSHRC (social sciences and humanities) than NSERC (natural sciences and engineering) recipients, suggesting it is not driven by a scarcity of women in natural sciences and engineering fields. Differences among fields within these two broad categories (NSE versus SSH) are more difficult to assess and should be explored further.

d. Starting salary: Megan measured the gender pay gap among faculty who joined the sunshine list in the same year. Faculty appearing for the first time on the sunshine list in 2016 already faced a $6900 gender pay gap. This suggests that differences in starting salary may be important contributors to the gender pay gap.

In summary, to a first approximation, about 44% of the gender pay gap at Ontario universities is due to seniority and about 56% is due to gender differences in starting salary. The difference in seniority should narrow as (disproportionately male) senior faculty retire, but universities should do more to address the gender gap in starting salaries.

Asher Cutter discussed “MISS MS MRS and MR: Titular self-identity in undergraduate performance”. Here is his summary:

We discussed the idea that inward and outward perceptions of one’s title might influence performance or perception of performance. I focused on female titles (Miss, Ms. and Mrs.) and conducted some exploratory analysis of undergraduate data for which titles based on higher degrees would not apply.

We began by discussing what different titles mean to people. An analysis by Lawton et al. (2003) of a sample of undergraduates and of an internet survey provided quantitative support for women predominantly electing to use different titles to convey marriage status. This analysis also showed that women who chose to use Miss tended to be young, suggesting that the choice of “Miss” versus “Ms.” may in part represent a proxy for one’s sense of maturity. However, only about 28% stated that they chose “Ms.” because they were single and only 27% because of their age.

We next discussed an analysis of how other people perceive different titles. The study by Dion (1987) that we discussed, coincidentally, was conducted on undergraduates at the University of Toronto. It found that people perceive women who use “Ms.” to have more “achievement motivation” and “social assertiveness” but less “interpersonal warmth” than do women who choose to use “Miss” as a title. A study by Etaugh et al. (1999) found that people’s perception of a woman’s sense of “agency” and “communion” also extends to her choice in surname upon marriage, implying that people judge women to have less agency but more sense of community if they change their surname to match their husband’s.

Given studies that demonstrate an effect of title choice on perceptions by others, we next discussed whether a woman’s title actually matters for performance. We considered academic performance in courses offered by EEB in connection with titles. In a set of nearly 4000 students, 48% had a title of “Miss” and 17% had a title of “Ms.” The ratio of Ms. : Miss increased among current students in year 1 to year 4. However, it is unclear whether the University sets a default title value, whether students choose a title upon applying to the University, or whether they have opportunity to change it. Moreover, students in year 1 and 2 include general life sciences students whereas those in years 3 and 4 are primarily pursuing a program of study offered by EEB; it is not clear whether the Ms:Miss ratio difference across years might reflect distinct choices among female students in EEB programs relative to other life science programs. In an analysis of cGPA among students in year 4, there was no significant difference among title groups (Miss, Ms., Mr.). Among 50 EEB courses, 3 showed a difference in the average grade achieved by individuals with a title of Miss versus Ms. (not significant after multiple test correction), and the difference was essentially zero in the largest two courses.

In conclusion, previous research finds consistent differences in people’s perceptions of the “achievement motivation” of women who choose different titles. However, our exploratory analysis found little to no difference in academic performance between women who choose titles of Miss versus Ms. Consequently, people’s perceptions about title choice do not seem to accurately reflect performance. While it is refreshing to see no obvious impact of title choice on performance, the potential mismatch with other people’s perceptions of performance could confer either positive or negative unintended consequences for individuals.

Tess Grainger presented on “Solutions for increasing diversity in STEM – do they work?” Here is her summary:

I discussed three approaches that have been used to increase diversity in STEM fields and in the workplace more generally: double-blind peer review, diversity hiring policies and maternity leave. I talked about the rational for each of these approaches, and outlined some studies that have tested their effectiveness.

Double-blind peer review:

In double-blind peer review, author information is masked from reviewers (in addition to reviewer information being masked from authors). This approach aims to eliminate reviewer biases associated with authors’ gender, ethnicity and seniority (Wenneras and Wold 2001). A study focused on ecological journals found that the proportion of published papers that had female first authors increased after double-blind review was implemented at Behavioral Ecology, while there was no change over the same time period at four journals that maintained single-blind review (Budden et al. 2008). However, a subsequent analysis of these data found no effect of double-blind review implementation on the representation of female first authors (Webb et al. 2008). Indeed, the findings of Budden et al. (2008) provoked a series of responses that ranged from complementary (Darling 2014) to critical (Whittaker 2008). In addition, while researchers consider double-blind peer review to be the most effective review method (Mulligan et al. 2012), a recent study of submissions to Nature group publications found that only 12% of authors actually choose double-blind review when given the option (Di Ranieri et al. 2017). These controversies and contradictions indicate that perhaps more data are need to identify the most effective way to implement double-blind peer review, and to understand when it is effective.

Faculty hiring policies:

These are hiring or search policies implemented at the university or the department level that are aimed at increasing the number of diverse applicants and hires. A study of 689 faculty searches examined whether these strategies are effective at increasing the diversity of hired faculty, with a focus on racial diversity (Smith et al. 2004). The policies examined in this study included a job description that explicitly mentioned diversity, a special hire strategy (e.g. diversity hire, spousal hire), and a diverse search committee (Smith et al. 2004). The authors found that only 26% of searches included one or more of these strategies, but that 71% of the cases when an under-represented group was hired, at least one of these policies had been implemented (Smith et al. 2004). The authors concluded that having at least one of these policies in place leads to more diverse hires.

Maternity leave:

Parental leave policies allow mothers to take a leave from their job after giving birth with the guarantee that their position will be held for them. Leaves can be either paid or unpaid, and range widely in length and compensation amount by country, province and company. A study that compared rates of mothers returning to work after having a child across the USA, Japan and the UK found that the proportion of mothers who returned increased substantially when even unpaid leave policies were in place (Waldfogel et al. 1999). Another study comparing rates of return to work before and after California’s Paid Family Leave Program was introduced similarly found that after the policy was in place, women were more likely to be working one year after giving birth (Baum and Rhum 2016).

Nicole Mideo presented “Letters of recommendation: data on gender bias”. Here is her summary:

We learned in a previous BREWS discussion about implicit bias and it’s clear that reference letters are an important part of progressing up any career ladder, so for this discussion we asked whether implicit bias could impact the quality of letters being written for males versus females.

We began with a discussion of the Trix & Psenka (2003) paper which qualitatively compared reference letters written for male and female applicants for faculty positions at US med schools. Letters for female applicants were shorter, included more grindstone words (e.g., “dependable”), fewer repeated standout words (e.g., “excellent”), included more ‘doubt raisers’ and gender terms.

Given BREWS’s emphasis on data, I collected my own, running a bunch of reference letters written by me and other professors in EEB through an online implicit bias detector. The estimated bias in letters I have written spanned from quite heavily female-biased language to quite heavily male-biased language. This was true for the full dataset too. With the help of John Stinchcombe, I analysed bias in these letters using mixed effects models, with a random effect of professor (anonymised) and fixed effects of gender, stage, prof gender, and all interactions. Two surprises emerged. (1) There was no significant effect of the gender of the person the letter was being written about. (2) There was a significant effect of the stage of that person. Letters for undergrads (specifically ones who the letter writer only knew from lecture courses) were more female-biased than letters for undergrads who had worked in the lab, while letters for grad students, postdocs, and faculty were further towards the male-biased end of the spectrum.

These results got me digging into the guts of the algorithm. Standout words, ability words (e.g., “intelligent”), and research words (e.g., “data”) are all categorised as male-biased, while grindstone words and teaching words (e.g., “course”) are categorised as female-biased. The algorithm counts the instances of these types of words in a letter, looks at the difference in numbers of male and female biased words and divides this difference by the total number of “gendered” words to get an estimate of bias. So, the stage results make a lot of sense. If a letter writer knows a student only from a lecture course, then there are likely to be a lot of teaching words that lead to “female-biased language”.

The algorithms cite a Schmader et al. (2007) study as inspiration, which statistically compared letters written for male and female applicants for faculty positions in chemistry and biochemistry. The results showed a significant reduction in standout adjectives in letters for females, but all other differences were non-significant or marginal, e.g., letters for females didn’t contain significantly fewer research or ability words nor did they contain significantly more teaching or grindstone words. Despite this, the online algorithms still assume a gender bias in all of those categories of words. This seems problematic and we discussed rerunning the analysis with only the standout adjectives as being ‘gendered’.

Finally, we discussed what evidence exists that the words categorized as female-biased result in weaker letters, which seems to be the underlying operating assumption; the Trix & Psenka study, however, only looked at letters for successful applicants! We felt that the specific job could alter the value of word categories (e.g., teaching words will be very valuable for applicants to teachers’ college), and that different fields will have different ‘cultures’ of letter writing.

Overall, we agreed that assessing implicit bias in reference letter writing requires more data and rigorous analysis.

Chelsea Rochman presented on “Women mentors and their contribution to gender composition in science”. Here is her summary:

We talked about the role of mentors in their contribution to gender equity. We specifically talked about the role of women mentors.

We began the discussion talking about these readings in the Atlantic about women mentors and bosses acting as “Queen Bees” (“Why do women bully each other at work?“, “Why women get criticized for being candid at work“, and “The Myth of the Queen Bee“).

We also read and dug into some work by Denon Start and Shannon McCauley about gender composition of academic research groups and a publication by Ellemers et al., 2004 called: Underrepresentation of women in science: differential commitment or the queen bee syndrome?

In short, the Atlantic articles discussed how women can sometimes act as Queen Bees:

This term was first defined by G.L. Staines, T.E. Jayaratne, and C. Tavris in 1973. It describes a woman in a position of authority who views or treats subordinates more critically if they are female. This phenomenon has been documented by several studies.

The question we wanted to ask is how this affects gender equity. Does this contribute to the underrepresentation of women in science?

Overall, we found that in the past women seemed to have a negative bias about women and their ability to succeed in academia. Over time, this seems to be disappearing as more women enter and succeed in academia. In addition, it seems that some gender bias in academia occurs at the applicant phase – suggesting that the more women apply, the more women will succeed. The good news – as more women enter academia and mentor women in academia, we get closer to achieving gender equity.

Rebecca Schalkowski & Rebecca Batstone presented “The relevance of socioeconomic background in academia”. Here are their summaries:

Rebecca S.: In my part of this talk I summarized different studies on factors relating to socioeconomic status which affect a child’s chances for academic success starting from the earliest development through the end of high school. These include occupation, income and education, which are all known to affect a child’s school achievements (American Educator Spring 2012, reviewed Sirin, 2005), IQ (Duncan et al, 1994), likelihood to do well in high school (Palardy, 2008) and attend college (Conley, 2001). Differences in household wealth as defined by the above factors have further been associated with affecting reading achievement (Aikens and Barbarin, 2008), math achievement (Chen et al., 1996), working memory (Noble et al., 2005), and the ability to regulate emotions and thought processes (Evans and Rosenbaum, 2008). The reason for families of lower socioeconomic status to suffer these effects as given by Daniel T. Willingham (American Educator, 2012) derive from lower access to opportunities. These opportunities can be classified by the three types of capital a person or family can have or lack: financial (e.g. books, tutors), human (e.g. skills & knowledge through education and experience of adults surrounding children) and social capital (e.g. connections or networks with people who have financial or human capital). This situation causes both lower resources and higher chronic stress to people from lower socioeconomic groups (Klerman, 1991, Conger et al., 1994) which will in turn lead to decreased academic performance, caused by physiological, psychological and economical disadvantages, causing them to drop out of high school up to 5 times more frequently and college (National Center for Education Statistics, 2008; Langhout, Drake, & Rosselli, 2009).

Rebecca B. focused on two recent large-scale studies: the first (Chetty et al. 2017, “Equality of Opportunity Project”) examined the socioeconomic background of students attending elite colleges in the states (spoiler alert: mostly rich kids), and the second (Clauset et al. 2015, Science Advances) examined a major predictor for who ends up landing a faculty position, namely, the prestige of candidate’s alma mater.

The Chetty et al. (2017) dataset comes from a project entitled the “College Mobility Report Cards”, and includes data from over 30 million students born in the US between 1980-1991 who graduated from colleges in the US between 1999-2013. The data compares the student’s income ranking in their early thirties and that of their parents to see whether attending a particular college was associated with the student’s upward mobility (going from a low-income bracket to a higher-income bracket). Two main findings were discussed: first, students attending “Ivy League” colleges (e.g., U. Chicago, Stanford, MIT), are 77 times more likely to come from families in the top 1% income distribution compared to the bottom 20% income distribution, indicating elite colleges are clearly failing at recruiting students from diverse socioeconomic backgrounds. Second, students from low-income families who attend elite universities receive earnings post-graduation equal to those from higher-income families, meaning that these colleges successfully “level the playing field” when it comes to financial success post-graduation, and also highlights that there is little cost to colleges for admitting students from low-income families. Unfortunately, student access to top colleges from the bottom 10 to 40% income distributions has not changed since 1999-2013, and funding for students to attend these elite colleges has declined 18% nation-wide since 2008, making it even less likely socioeconomic diversity will improve in the near future.

The second dataset (Clauset et al., 2015) consisted of 19,000 tenue-track or tenured faculty from 461 North American departmental or school-level academic units in three disciplines: computer science, business, and history. The goal of this project was to determine the factors that influence faculty hiring. One such factor emerges through “faculty hiring networks” – collective assessments whereby both the candidate being hired and the institution hiring must make a positive assessment of one another’s quality (e.g., based on teaching and research programs). The authors used faculty hiring networks to construct “social prestige” rankings for each institution, whereby institutions that disproportionately succeed in placing faculty and hire candidates from higher-ranked programs are characterized as being more prestigious compared to others. The authors found that across the disciplines examined, there exists a systematic bias in terms of who ends up getting a faculty placement. Only a quarter of the institutions included in the dataset are responsible for producing 71 to 86% of all tenue track faculty, and the size of the placements are not merely reflecting the size of the unit. The authors also found only 9 to 14% of faculty are placed at institutions with a higher prestige ranking than their doctorate, indicating steep prestige hierarchies, whereby less prestigious institutions hire candidates who graduated from more prestigious institutions in order to bolster their own prestige. As a result of this trend, most PhD’s slide down the prestige scale when they actually land a faculty position, and interestingly, women slide further down the scale compared to their male counterparts from the same institutions. Finally, more prestigious institutions also tend to be more central, well connected, and hold a more influential network position, which fosters the free exchange of ideas, and emphasizes the benefit of landing a position in such an institution. Linking back to the first dataset discussed, social inequality present at early academic levels (i.e., during undergrad) may be carried forward and even amplified at later academic stages. Programs that increase representation of students from diverse socioeconomic backgrounds at prestigious institutions are therefore extremely important to buffer against social inequality at latter stages, whereby the merit of a candidate is strongly influenced by the prestige of the university they graduated from.

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.

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).