Gender differences and bias in open source: pull request acceptance of women versus men
Josh Terrell,Andrew Kofink,Justin Middleton,Clarissa Rainear,Emerson Murphy-Hill,Chris Parnin,Jon W. Stallings +6 more
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This article is published in PeerJ.The article was published on 2017-05-01 and is currently open access. It has received 220 citations till now. The article focuses on the topics: Software development.read more
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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries.
TL;DR: A framework for identifying a broad range of menaces in the research and practices around social data is presented, including biases and inaccuracies at the source of the data, but also introduced during processing.
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What happens when software developers are (un)happy
TL;DR: In this paper, the authors study what happens when developers are happy and unhappy while developing software and find consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts.
Proceedings ArticleDOI
Gender diversity and women in software teams: how do they affect community smells?
TL;DR: It is concluded that women are instrumental to reducing community smells in software development teams and that the presence of women generally reduces the amount of community smells.
Proceedings ArticleDOI
Going farther together: the impact of social capital on sustained participation in open source
TL;DR: It is confirmed that while social capital is beneficial for prolonged engagement for both genders, women are at disadvantage in teams lacking diversity in expertise.
Proceedings ArticleDOI
Investigating the effects of gender bias on GitHub
Nasif Imtiaz,Justin Middleton,Joymallya Chakraborty,Neill Robson,Gina R. Bai,Emerson Murphy-Hill +5 more
TL;DR: The effects of gender bias are largely invisible on the GitHub platform itself, but there are still signals of women concentrating their work in fewer places and being more restrained in communication than men.
References
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Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
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The use of confidence or fiducial limits illustrated in the case of the binomial
C. J. Clopper,E. S. Pearson +1 more
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Gender differences in risk taking: A meta-analysis.
TL;DR: This paper conducted a meta-analysis of 150 studies in which the risk-taking tendencies of male and female participants were compared and found that the average effects for 14 out of 16 types of risk taking were significantly larger than 0 (indicating greater risk taking in male participants).
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MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
TL;DR: MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions.
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Science faculty’s subtle gender biases favor male students
TL;DR: In a randomized double-blind study, science faculty from research-intensive universities rated the application materials of a student as significantly more competent and hireable than the (identical) female applicant, and preexisting subtle bias against women played a moderating role.