Cited by 13Open Source Software InnovationsSoftware Engineering Techniques and PracticesSoftware Engineering Research

Relationship between diversity of collaborative group members’ race and ethnicity and the frequency of their collaborative contributions in GitHub

Shameer, Sheik, Rodríguez-Pérez, Gema, Nagappan, Meiyappan·2023·Empirical Software Engineering

Integrity signals

Registry

Crossref · 10.1007/s10664-023-10313-y

Cites

1 of 1 references verified0 citation errors · 0 not checkable

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Recomputing p-values needs the paper’s full text.

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  • Both open source and open collaboration in general are surprisingly lacking in diversity, with poor representation of non-male and non-white participants.

    “Yet both open source-and open collaboration in general-is surprisingly lacking in diversity, with poor representation of non-male and non-white participants [33, 73, 95, 109].”

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Authors
Shameer, S., Rodríguez-Pérez, G., & Nagappan, M.
Affiliations
  • University of Waterloo (CA)
  • University of British Columbia (CA)
Corresponding author
Rodríguez-Pérez, Gema
Venue
Empirical Software Engineering
Published
2023-05-20
Volume
28
Issue
4
ISSN
  • 1382-3256 (ISSN-L)
  • 1573-7616
OpenAlex
W4377140397
Cited by
13
References
1
Citations by year
2026: 4 · 2025: 8 · 2024: 1
FWCI
2.36
Type
journal article
Language
en
Open access
closed
Locations
Empirical Software Engineering (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Open Source Software Innovations
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Techniques and Practices
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Keywords
racial and ethnic diversity, open source software, cooperative groups, developer contributions, GitHub, group composition, pull requests

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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