The glass cliff refers to women being more likely to rise to positions of organizational leadership in times of crisis than in times of success, and men being more likely to achieve those positions in prosperous times. We examine the role that (a) a gendered history of leadership and (b) stereotypes about gender and leadership play in creating the glass cliff. In Expt 1, participants who read a...

Gender awareness in medicine consists of two attitudinal components: gender sensitivity and gender-role ideology. In this article, the development of a scale to measure these attitudes in Dutch medical students is described. After a pilot study and a feasibility study, 393 medical students in The Netherlands responded to a preliminary instrument consisting of 82 items (response rate 61.3%). Rel...

We found that judgments of a perceptually ambiguous social category, sexual orientation, varied as a function of a perceptually obvious social category, race. Sexual orientation judgments tend to exploit a heuristic of gender inversion that often promotes accuracy. We predicted that an orthogonal social category that is itself gendered, race, would impact both sexual orientation categorizations...

Six studies explored the overlap between racial and gender stereotypes, and the consequences of this overlap for interracial dating, leadership selection, and athletic participation. Two initial studies captured the explicit and implicit gender content of racial stereotypes: Compared with the White stereotype, the Asian stereotype was more feminine, whereas the Black stereotype was more masculi...

Selfies (self-portrait photographs often taken with a camera phone) are popularly used for selfpresentation in social media like Facebook and Instagram. These modern user-generated self-portraits have the potential to draw a more versatile picture of the genders instead of reproducing traditional gender stereotypes often presented in mainstream media and advertising. To investigate the degree o...

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturb...

Physicians’ recommendations often require prediction of patients’ intertemporal preferences. Towards the end of understanding the character of these predictions, we conduct laboratory experiments exploring the existence of stereotypes in intertemporal decision-making. Participants were asked to predict intertemporal decisions made by third parties described only by age and gender. We find evide...