Understanding the role of researchers who return to academia after prolonged inactivity, termed "comeback researchers", is crucial for developing inclusive models of scientific careers. This study investigates the structural and semantic behaviors of comeback researchers, focusing on their role in cross-disciplinary knowledge transfer and network reintegration. Using the AMiner citation dataset, we analyze 113,637 early-career researchers and identify 1,425 comeback cases based on a three-year-or-longer publication gap followed by renewed activity. We find that comeback researchers cite 126% more distinct communities and exhibit 7.6% higher bridging scores compared to dropouts. They also demonstrate 74% higher gap entropy, reflecting more irregular yet strategically impactful publication trajectories. Predictive models trained on these bridging- and entropy-based features achieve a 97% ROC-AUC, far outperforming the 54% ROC-AUC of baseline models using traditional metrics like publication count and h-index. Finally, we substantiate these results via a multi-lens validation. These findings highlight the unique contributions of comeback researchers and offer data-driven tools for their early identification and institutional support.

翻译：理解长期沉寂后重返学术界的"回归研究者"的角色，对于构建包容性的科学生涯模型至关重要。本研究考察回归研究者的结构与语义行为，重点关注其在跨学科知识转移和网络重构中的作用。基于AMiner引文数据集，我们分析了113,637名早期职业研究者，并根据三年及以上发表空白期后的学术活动恢复情况，识别出1,425个回归案例。研究发现，与退出者相比，回归研究者引用的学术社群多样性高出126%，桥接分数高出7.6%。其间隔熵值高出74%，反映出更具不规则性但战略影响力更强的发表轨迹。基于这些桥接与熵特征训练的预测模型达到97%的ROC-AUC，显著优于使用传统指标（如发表数量与h指数）的基线模型（54% ROC-AUC）。最后，我们通过多视角验证证实了这些结果。这些发现凸显了回归研究者的独特贡献，并为其早期识别与制度支持提供了数据驱动的工具。