# BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals

arXiv: https://arxiv.org/abs/2609.19422 · HTML: https://mlsanity.com/paper/2609.19422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 55 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Toxicity Detection with Generative Prompt-based Inference](https://mlsanity.com/paper/2205.12390/) (2022)
- [Mining Valence, Arousal, and Dominance - Possibilities for Detecting Burnout and Productivity?](https://mlsanity.com/paper/1603.04287/) (2016)
- [A Novel Approach for Estimating Truck Factors](https://mlsanity.com/paper/1604.06766/) (2016)
- [Bridging Discrete and Continuous: A Multimodal Strategy for Complex Emotion Detection](https://mlsanity.com/paper/2409.07901/) (2024)
- [The "Shut the f**k up" Phenomenon: Characterizing Incivility in Open Source Code Review Discussions](https://mlsanity.com/paper/2108.09905/) (2021)
- [An Exploratory Study of Project Activity Changepoints in Open Source Software Evolution](https://mlsanity.com/paper/2103.11013/) (2021)

## Abstract (arXiv)

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.