We adopt the general bias-testing framework from<cite> Caliskan et al. (2017)</cite> , but we span our study over multiple dimensions: (1) corpora -we analyze the consistency of biases across distributional vectors induced from different types of text; (2) embedding models -we compare biases across distributional vectors induced by different embedding models (on the same corpora); and (3) languageswe measure biases for word embeddings of different languages, trained from comparable corpora. Furthermore, unlike<cite> Caliskan et al. (2017)</cite> , we test whether biases depend on the selection of the similarity metric.