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BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals — arXiv:2609.19422

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.19422
License (as archived): CC0-1.0
Archived: 2026-10-10T03:48:44.540Z
SHA-256 of the archived text: 84975ff0d9d8be80b408fea9d02f6bfbf3871ffd44ba2b8a21ddedda17f5791a
Stable URL of this page: https://metraipolis.com/documento/dc_2ccfa6808ff4

How to cite

BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals — arXiv:2609.19422 — arXiv (en), https://arxiv.org/abs/2609.19422 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2ccfa6808ff4 (Metraipolis archive dc_2ccfa6808ff4, sha256 84975ff0d9d8be80b408fea9d02f6bfbf3871ffd44ba2b8a21ddedda17f5791a)

BibTeX

@misc{dc2ccfa6808ff4,
  title = {BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals — arXiv:2609.19422},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.19422},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2ccfa6808ff4; sha256 84975ff0d9d8be80b408fea9d02f6bfbf3871ffd44ba2b8a21ddedda17f5791a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.19422. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Burnout is a chronic occupational syndrome, and open source is close to a worst case for it: maintainers absorb unbounded demand with no manager to reallocate work and no organization to notice decline. The cost is not only personal. Burnout precedes withdrawal, and in projects sustained by a handful of maintainers, one departure can break infrastructure that thousands of downstream systems depend on. Yet the field has no way to see it coming: self-report inventories, the only existing measure, miss exactly the contributors most in need of detection and cannot be applied retroactively, so the field cannot even ask how common burnout is or what helps. We present BurnRiSc, a framework that operationalizes the Oldenburg Burnout Inventory's two dimensions, exhaustion and disengagement, as 14 behavioral and linguistic signals computed from GitHub activity and scored against each contributor's own history. The signals aggregate into two weighted dimension scores, with weights learned from labeled cases, and average into a monthly Burnout Risk Score (BRS). In a preliminary evaluation across 68 contributors in ten repositories (ten disclosed burnout cases, twelve comparable-volume collapses, and 46 comparison contributors), sustained BRS elevation precedes 6 of 10 disclosures by 6-15 months, 8 of 10 when adding peak BRS as a second criterion, and 10 of 10 over any prior time frame. We thus present BurnRiSc as evidence that burnout is screenable from public data.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-18 in cs.CL).
Categorie arXiv: Computers and Society (cs.CY) ; Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.19422 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-10T03:24:08.187Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-18?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).