# A Human Behavioral Baseline for Collective Governance in Software Projects

arXiv: https://arxiv.org/abs/2510.08956 · HTML: https://mlsanity.com/paper/2510.08956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Power to the People? Opportunities and Challenges for Participatory AI](https://mlsanity.com/paper/2209.07572/) (2022)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [`It is currently hodgepodge'': Examining AI/ML Practitioners' Challenges during Co-production of Responsible AI Values](https://mlsanity.com/paper/2307.10221/) (2023)
- [Generative AI for Pull Request Descriptions: Adoption, Impact, and Developer Interventions](https://mlsanity.com/paper/2402.08967/) (2024)
- [Quality Gatekeepers: Investigating the Effects ofCode Review Bots on Pull Request Activities](https://mlsanity.com/paper/2103.13547/) (2021)
- [Algorithmic Collective Action with Two Collectives](https://mlsanity.com/paper/2505.00195/) (2025)
- [AI in Software Engineering: A Survey on Project Management Applications](https://mlsanity.com/paper/2307.15224/) (2023)
- [NLP4Gov: A Comprehensive Library for Computational Policy Analysis](https://mlsanity.com/paper/2404.03206/) (2024)
- [Do We Run How We Say We Run? Formalization and Practice of Governance in OSS Communities](https://mlsanity.com/paper/2309.14245/) (2023)
- [GitHub OSS Governance File Dataset](https://mlsanity.com/paper/2304.00460/) (2023)

## Abstract (arXiv)

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.