Thanks to Greg Martin for this guest post! Greg has been writing interesting and important material recently concerning gender inequity in mathematics. For the eMentoring network, he writes about mentorship and gender. What follows are Greg’s words.
Mentorship and Gender
Personal, dedicated mentorship is an extremely important part of postgraduate education and the academic career. So are mentor-like networks of more senior members of the discipline, as well as supportive networks of academic peers. Those who don’t have adequate access to these resources are at a significant disadvantage, even with full use of “official” resources such as courses and job postings. In an extreme case, a position might not even be officially publicized before being offered to the student of a close colleague. At the very least, consultation with someone who has broader experience and academic success can help an up-and-coming mathematician to optimize their allocation of energy.
Unfortunately, these mentorship opportunities are not equally available to everyone in mathematics; in particular, there is a systemic bias against female students and mathematicians. Implicit biases cause us (all of us) to systematically undervalue women in mathematics – female applicants to graduate school, female speakers at conferences, female authors of papers, female faculty members being evaluated for tenure. In particular, they cause us to be less likely to devote our time to mentoring women.
For example, professors at US universities who are contacted by students interested in their doctoral program respond more frequently to men than to women (and, for that matter, more frequently to Caucasians than to applicants of other ethnicities)—and this propensity is exaggerated in more lucrative fields and at more prestigious institutions [5]. Both female and male faculty members rate students’ application materials differently when the applicant is female or male: even with identical files, the female applicant is judged to be less competent, and male applicants are offered a 14% higher starting salary and more mentoring on average than female applicants [6]. The annotated bibliography [4] contains a wealth of references for those interested in learning further about biases against women in mathematics and science.
We are also unconsciously drawn to people who are like us, who have a higher likelihood of shared experience with us. In addition to being implicitly regarded as unsuitable for scientific positions, women have fewer female contacts in positions of authority, which means that they are disadvantaged by having less influential networks [3]. In particular, the dearth of senior female mathematicians means that younger women seeking mentorship are at a further disadvantage – in terms of both the actual choices of mentor, and the psychological toll of feeling like an outsider. This disadvantage is one of many factors leading to our existing “leaky pipeline”: the higher the academic rank, the smaller the percentage of women (see [1] and [7]).
Then what are our action items? For those of us in a position to offer mentorship, we must be cognizant of these biases, and look twice at files by female applicants. We should recognize that some of these excellent applicants will be available specifically because they’ve been unfairly passed over by other people – so we should be assertive about making contact with them! And for women trying to seek mentorship, be ready for the fact that it might be harder for you than you deserve, but it is well worth the effort. Be proactive about seeking contact with established mathematicians and with peers, and trust your instincts as to who will be truly supportive and understanding about the inequitable challenge faced by female scientists.
[1] R. Cleary, J. W. Maxwell, and C. Rose, Fall 2012 departmental profile report, Notices of the AMS 61 (2014), no. 2, 158–167.
[2] D.J. Dean and J.B. Koster, Mentoring and networking, Equitable Solutions for Retaining a Robust STEM Workforce: Beyond best practices, Academic Press, 2014, Chapter 7.
[3] R. J. Ely, H. Ibarra, and D. M. Kolb, Taking gender into account: theory and design for women’s leadership development programs, Academy of Management Learning & Education 10 (2011), no. 3, 474–493.
[4] G. Martin, An annotated bibliography of work related to gender in science, 2015. http://www.math.ubc.ca/~gerg/index.shtml?abstract=ABWRGS
[5] K. L. Milkman, M. Akinola, and D. Chugh, What happens before? a field experiment exploring how pay and
representation differentially shape bias on the pathway into organizations, preprint.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2063742
[6] C. A. Moss-Racusin, J. F. Dovidio, V. L. Brescoll, M. J. Graham, and J. Handelsman, Science faculty’s subtle gender biases favor male students, Proceedings of the National Academy of Science of the USA 109 (2012), no.
41, 16474–16479.
[7] WISELI, Advancing women in science and engineering: advice to the top, Women in Science & Engineering Leadership Institute (Madison). http://wiseli.engr.wisc.edu/docs/AdviceTopBrochure.pdf
[8] WISELI, Fostering success for women in science and engineering, Women in Science & Engineering Leadership Institute (Madison). http://wiseli.engr.wisc.edu/docs/FosteringSuccessBrochure.pdf
