Abstract:
Women in traditionally male-typed domains tend to be evaluated more negatively than their male counterparts (Rudman, Moss-Racusin, Glick, & Phelan, 2012) due to persistent, deeply ingrained cultural beleifs about the roles males and females should occupy (Rudman, Moss-Racusin, Phelan, & Nauts, 2012). Diversity idelogies based on either ignoring gender differences (gender-blindness) or acknowledging gender differences (gender-awareness), respectively, have developed as strategies for reducing such gender biases (Koenig & Richeson, 2010), with gender-blindness being more effective for reducing stereotypes about women in male-typed domains (Martin & Phillips, in press). Because it remains unclear how it interacts with gender prejudice in a workplace setting, the present study experimetnally examined whether gender-blindness, compared to gender-awareness and a control, would better mitigate negative evaluations of a female expert in a male-typed domain. A total of 199 undergraduate students were randomly assigned to read about either gender-blindness, gender-awareness, or big data (control), and then were then randomly assigned to read about either a male or female computer science expert. Finally, all participants read a message from the expert recommending a programming language and then asked to evaluate the expert and their message. Results of a 3
PRIMING FOR GENDER-BLINDNESS 2
(gender-blindness v. gender-awareness v. control) by 2 (female expert v. male expert) factorial ANOVA reveleaded that evaluations of the male and female expert did not differ between participants primed with gender-blindness, gender-awareness, or a control topic. Future research may consider incorporating more domain-specific evaluations or using a non-student sample.
