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Stephen V. David

Publications and source records attributed to Stephen V. David.

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Impact of gender on the formation and outcome of mentoring relationships in academic research

Despite increasing representation in graduate training programs, a disproportionate number of women leave academic research before obtaining an independent position. To understand factors underlying this trend, we analyzed a multidisciplinary database of Ph.D. and postdoctoral mentoring relationships covering the years 2000-2020, focusing on data from the life sciences. Student and mentor gender are both associated with differences in rates of student's continuation to independent mentor positions of their own. Although trainees of women mentors are less likely to take on independent positions than trainees of men mentors, this effect is reduced substantially after controlling for several measurements of mentor status. Thus the effect of mentor gender can be explained at least partially by gender disparities in social and financial resources available to mentors. Because trainees and mentors tend to be of the same gender, this association between mentor gender and academic continuation disproportionately impacts women trainees. On average, gender homophily in graduate training is unrelated to mentor status. A notable exception to this trend is the special case of scientists having been granted an outstanding distinction, evidenced by membership in the National Academy of Sciences, being a grantee of the Howard Hughes Medical Institute, or having been awarded the Nobel Prize. This group of mentors trains men graduate students at higher rates than their most successful colleagues. These results suggest that, in addition to other factors that limit career choices for women trainees, gender inequities in mentors' access to resources and prestige contribute to women's attrition from independent research positions.

cs.SI

A dataset of mentorship in science with semantic and demographic estimations

Mentorship in science is crucial for topic choice, career decisions, and the success of mentees and mentors. Typically, researchers who study mentorship use article co-authorship and doctoral dissertation datasets. However, available datasets of this type focus on narrow selections of fields and miss out on early career and non-publication-related interactions. Here, we describe MENTORSHIP, a crowdsourced dataset of 743176 mentorship relationships among 738989 scientists across 112 fields that avoids these shortcomings. We enrich the scientists' profiles with publication data from the Microsoft Academic Graph and "semantic" representations of research using deep learning content analysis. Because gender and race have become critical dimensions when analyzing mentorship and disparities in science, we also provide estimations of these factors. We perform extensive validations of the profile--publication matching, semantic content, and demographic inferences. We anticipate this dataset will spur the study of mentorship in science and deepen our understanding of its role in scientists' career outcomes.

cs.DL