We introduce reputable citations (RC), a method to screen and segment a collection of papers by decoupling popularity and influence. We demonstrate RC using recent works published in a large set of mathematics journals from Clarivate's Incites Essential Science Indicators, leveraging Clarivate's Web of Science for citation reports and assigning prestige values to institutions based on well-known international rankings. We compare researchers drawn from two samples: highly cited researchers (HC) and mathematicians whose influence is acknowledged by peers (Control). RC scores distinguish the influence of researchers beyond citations, revealing highly cited mathematical work of modest influence. The control group, comprising peer-acknowledged researchers, dominates the top tier of RC scores despite having fewer total citations than the HC group. Influence, as recognized by peers, does not always correlate with high citation counts, and RC scores offer a nuanced distinction between the two. With development, RC scores could automate screening of citations to identify exceptional and influential research, while addressing manipulative practices. The first application of RC reveals mathematics works that may be cited for reasons unrelated to genuine research advancements, suggesting a need for continued development of this method to mitigate such trends.

翻译：本文提出一种名为"声誉引用"（RC）的方法，通过解耦流行度与影响力来实现文献集的筛选与分类。我们运用该方法对科睿唯安InCites基本科学指标数据库中数学期刊近期发表的文献进行实证分析：利用Web of Science获取引证报告，依据国际公认排名为机构赋予声望值。研究选取两组样本进行对比：高被引研究者（HC）和同行认可的影响力学者（对照组）。RC评分能够超越引文数量区分研究者的影响力，揭示出部分高被引数学成果的实际影响有限。由同行认可学者构成的对照组虽总被引量低于HC组，却在RC评分顶端占据主导地位。同行认可的影响力并不总与高被引次数相关，RC评分可对二者进行精细化区分。经完善后，RC评分可自动化实现引文筛选以识别卓越且有影响力的研究，同时应对操纵性引用行为。RC方法的首次应用揭示了部分数学成果可能因非学术进步因素被引用，表明该方法需持续改进以遏制此类趋势。