Gender representation in constructed example sentences | SOAS University of London
Channel: SOAS University of London
Date: 25/03/2021

Note: This talk has not gone through a process of peer review, and findings should therefore be treated as preliminary and subject to change.

How to cite: Kotek, Hadas. 2021. Gender Representation in Constructed Example Sentences. Talk presented at SOAS Linguistics Webinars 2021-03-24.

DOI: 10.5281/zenodo.4633948

SOAS Linguistics Webinars: Gender Representation in Constructed Example Sentences
Hadas Kotek (MIT)
March 24, 2021

Abstract: This talk surveys two ongoing projects concerned with the representation of women in example sentences in linguistics. (Collaborations with Paola Cépeda, Katharina Pabst, Kristen Syrett, Sarah Babinski, Rikker Dockum, and Christopher Geissler.) The first half of the talk focuses on constructed example sentences in syntax textbooks. Following the 20th anniversary of Macaulay & Brice (1997: M&B)’s survey of example sentences in 11 syntax textbooks, we present an analysis of 6 recently published textbooks. We find that the gender skew and stereotypes reported in M&B are almost all still present: Among other findings, men are twice as likely to occur as subjects, agents, and experiencers. Men additionally engage in intellectual activities, have diverse occupations, and engage in violent activities. The second project examines example sentences published in all papers published in Language, Linguistic Inquiry, and Natural Language & Linguistic Theory over the past 20 years. We find striking similarities to prior work, but are able to provide greater detail. Among our findings: men are more than twice as likely to occur as subjects, agents, and experiencers; men engage in violence and exhibit negative emotion while women are referred to using kinship terms and express positive emotion. These trends remain stable over time and across journals, and do not vary by the language of example. I conclude the talk by discussing the importance of raising awareness of biases in research and teaching materials among individual researchers and (especially) instructors, as well as how we can improve on the current standards.