AI 中文总结
该研究发现了“可读性差距”这一新的不平等模式，即基于姓名推断性别的公平干预会因姓名的文化差异，使西方姓名女性受益而东亚姓名女性受损，揭示了全球科学公平体系的文化公平性问题。
AI 中文摘要
推动科学领域性别公平的努力日益依赖基于姓名的推断来量化代表性、指导政策与行为。然而，表明性别的语言线索因文化而异，当姓名被音译为英文时往往会被掩盖。本文我们识别出一种称为“可读性差距”的模式：当从姓名推断性别时，公平干预会系统性地惠及姓名表明性别的女性，却忽略那些姓名在音译中丢失此类线索的女性。通过观察性研究与实验证据，我们展示了这一差距如何重塑科学领域的认可。对引文多样性声明（一种新兴实践，作者会报告其参考文献列表经算法估算的性别构成）的分析显示，采用该实践的论文引用女性的频率更高，但收益几乎完全归于拥有表明性别的西方姓名的作者。相比之下，那些姓名在英文音译中丢失性别线索的女性（主要是拥有东亚姓名的女性），在这些相同论文中获得的引文更少。两项预先注册的实验（样本量N=2250）证实了这一模式并明确了其机制：是语言可读性而非文化陌生度，决定了谁被认可为女性、谁能从支持科学界女性的政策中受益。总体而言，这些发现揭示了嵌入全球公平基础设施中此前未被认识到的一层不平等。随着科学日益全球化、公平努力日益算法化，可读性差距凸显了不均衡的身份认可如何重塑公平。在全球认可体系中，公平不仅取决于政策整体是否有效，还取决于它们在不同文化间是否公平。
英文摘要
Efforts to promote gender equity in science increasingly rely on name-based inference to quantify representation and guide policy and behavior. Yet linguistic cues that signal gender vary across cultures and are often obscured when names are transliterated into English. Here we identify a pattern we call the "legibility gap": when gender is inferred from names, equity interventions systematically benefit women whose names signal gender while bypassing those whose names lose such cues in translation. Using both observational and experimental evidence, we show how this gap reshapes recognition in science. Analyzing citation diversity statements-an emerging practice in which authors report the algorithmically estimated gender composition of their reference lists-we find that papers that include this practice cite women more frequently, but the gains accrue almost entirely to authors with gender-signaling Western names. By contrast, women whose names lose gender cues in English transliteration, predominantly those with East Asian names, receive fewer citations in these same papers. Two preregistered experiments (N = 2,250) corroborate this pattern and identify its mechanism: linguistic legibility, not cultural unfamiliarity, determines who is recognized as a woman and who benefits from policies designed to support women in science. Overall, these findings expose a previously unrecognized layer of inequity embedded in global equity infrastructures. As science becomes increasingly global and equity efforts increasingly algorithmic, the legibility gap reveals how uneven identity recognition reshapes fairness. In global systems of recognition, equity depends not only on whether policies are effective on average, but also on whether they are equitable across cultures.
