标题： 用引文距离区分真实影响和超高产性 显示英文标题

标题： Distinguishing True Influence from Hyperprolificity with Citation Distance

摘要： 准确评估学术影响力对于公平的学术评价至关重要，然而传统的基于出版物和引用次数的文献计量指标往往更倾向于高产作者，而不是那些具有更深层次和长期影响的学者。 我们提出了x指数，这是一种新颖的基于引用的度量方法，它将引用概念化为知识传播的过程，并结合引用距离来反映学术工作的结构影响范围。 通过根据引用者与被引者之间的协作接近程度加权引用，x指数能够捕捉到演化中的学术网络中影响力的深度和广度。 实证分析表明，x指数显著提高了图灵奖获得者的排名，同时降低了高产作者的排名，更好地将排名与公认的学术成就对齐。 它还显示出在早期职业研究人员中具有优越的区分能力，并揭示了对机构研究质量更强的敏感性。 这些结果表明，x指数为现有的度量标准提供了一个更加公平和前瞻性的替代方案，在人才识别、资金决策和学术推荐系统中有实际应用价值。 显示英文摘要

摘要： Accurately evaluating scholarly influence is essential for fair academic assessment, yet traditional bibliometric indicators - dominated by publication and citation counts - often favor hyperprolific authors over those with deeper, long-term impact. We propose the x-index, a novel citation-based metric that conceptualizes citation as a process of knowledge diffusion and incorporates citation distance to reflect the structural reach of scholarly work. By weighting citations according to the collaborative proximity between citing and cited authors, the x-index captures both the depth and breadth of influence within evolving academic networks. Empirical analyses show that the x-index significantly improves the rankings of Turing Award recipients while reducing those of hyperprolific authors, better aligning rankings with recognized academic merit. It also demonstrates superior discriminatory power among early-career researchers and reveals stronger sensitivity to institutional research quality. These results suggest that the x-index offers a more equitable and forward-looking alternative to existing metrics, with practical applications in talent identification, funding decisions, and academic recommendation systems.