Scientific fields are perceived of as more masculine than feminine and stereotypes of scientists are more closely associated with stereotypes of men than of women (Carli et al., 2016). Supporting this point, Elmore, Block, Bowers and Dutt (2019) uncovered 31 topics from letters of recommendation for post-doctoral applicants in the geosciences and found that these topics described male and female applicants differently and, in a manner consistent with gender stereotypes. As the number of women in the geosciences declines further up the academic ladder (National Science Foundation, 2017) and as letters of recommendation play an important role in academic hiring (Abbott et al. 2010), it is important to understand if the use of gendered topics in these letters may reduce the likelihood of female advancement. Thus, in the present study we gathered questionnaire data from 250 geoscience researchers and scientists asking them to rate and rank the topics uncovered by Elmore et al. (2019) in terms of their importance when making hiring decisions. Results showed geoscientists valued research productivity and publishing over being a teacher, student, or department citizen. Topics expressed more in letters for male applicants in Elmore et al. (2019) were rated as significantly more important when making a hiring decision than topics expressed more in letters for female applicants. Further, the male topics identified in Elmore et al. (2019) were ranked more often as the most important topics and less often as least important topics when making a hiring decision compared to the female topics. Finally, 68% of participants indicated they attribute 50% or more of their hiring decision to information found in letters of recommendation, underscoring the importance of letters of recommendation to career advancement in the geosciences.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/d8-5gc1-7q70 |
| Date | January 2020 |
| Creators | Elmore, Joshua |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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