BurnRiSc：基于公共仓库信号的开源非侵入性倦怠筛查
BurnRiSc: Toward Non-Invasive Burnout Screening in Open Source from Public Repository Signals
- Queen’s University（女王大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对开源维护者倦怠难以预警的问题，提出BurnRiSc框架，利用GitHub活动计算14个行为与语言信号，生成月度倦怠风险评分，初步实验显示可提前数月识别多数倦怠案例。
AI中文摘要：
倦怠是一种慢性职业综合征，而开源环境几乎是最糟糕的情形：维护者承受无限制的需求，没有管理者重新分配工作，也没有组织注意到状态下滑。其代价不仅是个人的。倦怠先于退出行为，而在由少数维护者支撑的项目中，一个人的离开可能破坏数千个下游系统所依赖的基础设施。然而，该领域目前无法预见其发生：自我报告量表作为唯一现有的测量手段，恰恰遗漏了最需要被检测的贡献者，且无法追溯应用，因此该领域甚至无法了解倦怠的普遍程度或哪些因素有所帮助。我们提出BurnRiSc框架，将奥尔登堡倦怠量表的两个维度——耗竭和疏离——操作化为14个行为和语言信号，这些信号从GitHub活动中计算得出，并针对每位贡献者自身的历史记录进行评分。这些信号聚合成两个加权维度得分，权重从标注案例中学习，并平均为月度倦怠风险评分（BRS）。在一项初步评估中，覆盖十个仓库中的68名贡献者（十个已披露的倦怠案例、十二个可比规模的活跃度骤降案例以及四十六名对照贡献者），持续的BRS升高在6至10个披露案例中的6个中提前6至15个月出现，当添加峰值BRS作为第二标准时达到10例中的8例，而在任何先前时间范围内达到10例中的10例。因此，我们将BurnRiSc作为证据，表明倦怠可以从公共数据中进行筛查。
英文摘要：
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.
