Gender differences and bias in open source: pull request acceptance of women versus men

Author:

Terrell Josh1,Kofink Andrew2,Middleton Justin2,Rainear Clarissa2,Murphy-Hill Emerson2,Parnin Chris2,Stallings Jon3

Affiliation:

1. Department of Computer Science, California Polytechnic State University—San Luis Obispo, San Luis Obispo, CA, United States

2. Department of Computer Science, North Carolina State University, Raleigh, NC, United States

3. Department of Statistics, North Carolina State University, Raleigh, NC, United States

Abstract

Biases against women in the workplace have been documented in a variety of studies. This paper presents a large scale study on gender bias, where we compare acceptance rates of contributions from men versus women in an open source software community. Surprisingly, our results show that women’s contributions tend to be accepted more often than men’s. However, for contributors who are outsiders to a project and their gender is identifiable, men’s acceptance rates are higher. Our results suggest that although women on GitHub may be more competent overall, bias against them exists nonetheless.

Funder

National Science Foundation

Publisher

PeerJ

Subject

General Computer Science

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4. Controlling the false discovery rate: a practical and powerful approach to multiple testing;Benjamini;Journal of the Royal Statistical Society. Series B (Methodological),1995

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