Ld differences in its endorsement are centrally involved in the emergence of gender gaps. On the one hand, it seems reasonable to suppose that fields whose views of women are more negative will be less diverse. On the other hand, since the “brilliance = males” stereotype is part of the common ground shared by most members of our culture, its local levels in a discipline may be less important than women’s BMS-986020 site awareness that–due to this pervasive stereotype– their intellectual capacities could potentially be called into question. In this case, variation across fields in endorsement of this background stereotype may not be straightforwardly related to gender diversity. The RateMyProfessors.com data may allow us to differentiate between these alternatives, so we investigate whether fields with stronger stereotypes (measured as greater use of “brilliant” and “genius” for male vs. female instructors) are also less diverse.Methods Data on PhD RepresentationThe proportions of female, African American, and Asian American PhDs were obtained from the National Science Foundation’s (NSF) Survey of Earned Doctorates [39]. Note that the data publicly available from the NSF do not break down these statistics by gender and race simultaneously; only separate breakdowns by gender and by race are provided in the public-use data. As a result, we did not investigate the intersection of these dimensions in our analyses. For example, fpsyg.2016.01503 when we explored what predicts the representation of African Americans, we included both males and females in our analyses.Data on Bachelor’s RepresentationThe proportions of female, African American, and Asian American bachelor’s degrees were obtained from NSF’s Science and Engineering Indicators [40]. Because NSF does not report data on non-STEM disciplines at the bachelor’s level, our order MS-275 analysis of bachelor’s degrees was limited to STEM disciplines. Also, as with the Survey of Earned Doctorates [39], the public-use data on bachelor’s degrees are broken down by gender and race separately, not by the intersection of these dimensions.Brilliance Language MeasureThe main independent variable–our new language-based measure of a field’s emphasis on raw intellectual talent–was calculated using the online Gendered Language Tool [6], which reportsPLOS ONE | DOI:10.1371/journal.pone.0150194 March 3,5 /”Brilliant” “Genius” on RateMyProfessors Predict a Field’s DiversityFig 1. Frequency of “genius” and “brilliant” per millions of words of text on RateMyProfessors.com, split by gender and discipline. doi:10.1371/journal.pone.0150194.gthe number of uses of any given word per million words in RateMyProfessors.com reviews. More precisely, the tool reports a word’s frequency in each of 25 fields, separately for reviews of male and female instructors (see Fig 1). The tool searches over 14 million reviews from hundreds of different colleges and universities. The top three contributors to RateMyProfessors. com fpsyg.2016.01448 (and thus to the frequencies reported by the Gendered Language Tool) are the University of Central Florida, Miami Dade College, and San Diego State University. The data collected specifically for this study (namely, the word counts from the Gendered Language Tool) are completely anonymous and publicly available. Thus, the process of collecting them was exempt from review by an ethics committee. We computed a brilliance language score for each discipline by (1) standardizing the frequencies of the words “brilliant” and (separately) “genius” fo.Ld differences in its endorsement are centrally involved in the emergence of gender gaps. On the one hand, it seems reasonable to suppose that fields whose views of women are more negative will be less diverse. On the other hand, since the “brilliance = males” stereotype is part of the common ground shared by most members of our culture, its local levels in a discipline may be less important than women’s awareness that–due to this pervasive stereotype– their intellectual capacities could potentially be called into question. In this case, variation across fields in endorsement of this background stereotype may not be straightforwardly related to gender diversity. The RateMyProfessors.com data may allow us to differentiate between these alternatives, so we investigate whether fields with stronger stereotypes (measured as greater use of “brilliant” and “genius” for male vs. female instructors) are also less diverse.Methods Data on PhD RepresentationThe proportions of female, African American, and Asian American PhDs were obtained from the National Science Foundation’s (NSF) Survey of Earned Doctorates [39]. Note that the data publicly available from the NSF do not break down these statistics by gender and race simultaneously; only separate breakdowns by gender and by race are provided in the public-use data. As a result, we did not investigate the intersection of these dimensions in our analyses. For example, fpsyg.2016.01503 when we explored what predicts the representation of African Americans, we included both males and females in our analyses.Data on Bachelor’s RepresentationThe proportions of female, African American, and Asian American bachelor’s degrees were obtained from NSF’s Science and Engineering Indicators [40]. Because NSF does not report data on non-STEM disciplines at the bachelor’s level, our analysis of bachelor’s degrees was limited to STEM disciplines. Also, as with the Survey of Earned Doctorates [39], the public-use data on bachelor’s degrees are broken down by gender and race separately, not by the intersection of these dimensions.Brilliance Language MeasureThe main independent variable–our new language-based measure of a field’s emphasis on raw intellectual talent–was calculated using the online Gendered Language Tool [6], which reportsPLOS ONE | DOI:10.1371/journal.pone.0150194 March 3,5 /”Brilliant” “Genius” on RateMyProfessors Predict a Field’s DiversityFig 1. Frequency of “genius” and “brilliant” per millions of words of text on RateMyProfessors.com, split by gender and discipline. doi:10.1371/journal.pone.0150194.gthe number of uses of any given word per million words in RateMyProfessors.com reviews. More precisely, the tool reports a word’s frequency in each of 25 fields, separately for reviews of male and female instructors (see Fig 1). The tool searches over 14 million reviews from hundreds of different colleges and universities. The top three contributors to RateMyProfessors. com fpsyg.2016.01448 (and thus to the frequencies reported by the Gendered Language Tool) are the University of Central Florida, Miami Dade College, and San Diego State University. The data collected specifically for this study (namely, the word counts from the Gendered Language Tool) are completely anonymous and publicly available. Thus, the process of collecting them was exempt from review by an ethics committee. We computed a brilliance language score for each discipline by (1) standardizing the frequencies of the words “brilliant” and (separately) “genius” fo.