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The rapid growth of open source software necessitates a deeper understanding of moderation and governance methods currently used within these projects. The code of conduct, a set of rules articulating standard behavior and responsibilities for participation within a community, is becoming an increasingly common policy document in open source software projects for setting project norms of behavior and discouraging negative or harassing comments and conversation. This study describes the conversations around adopting and crafting a code of conduct as well as those utilizing code of conduct for community governance. We conduct a qualitative analysis of a random sample of GitHub issues that involve the code of conduct. We find that codes of conduct are used both proactively and reactively to govern community behavior in project issues. Oftentimes, the initial addition of a code of conduct does not involve much community participation and input. However, a controversial moderation act is capable of inciting mass community feedback and backlash. Project maintainers balance the tension between disciplining potentially offensive forms of speech and encouraging broad and inclusive participation. These results have implications for the design of inclusive and effective governance practices for open source software communities.
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A Human Behavioral Baseline for Collective Governance in Software Projects
We study how open source communities describe participation and control through version controlled governance documents. Using a corpus of 710 projects with paired snapshots, we parse text into actors, rules, actions, and objects, then group them and measure change with entropy for evenness, richness for diversity, and Jensen Shannon divergence for drift. Projects define more roles and more actions over time, and these are distributed more evenly, while the composition of rules remains stable. These findings indicate that governance grows by expanding and balancing categories of participation without major shifts in prescriptive force. The analysis provides a reproducible baseline for evaluating whether future AI mediated workflows concentrate or redistribute authority.
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- Award ID(s):
- 2020751
- PAR ID:
- 10639624
- Publisher / Repository:
- NeurIPS Algorithmic Collective Action Workshop
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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