追踪对可疑研究的高度关注：资助者尽职调查的案例
Tracing high-profile attention to questionable research as a case for funder due diligence
- Digital Science（数字科学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究以2022年发现的作者售卖网络为案例，发现可疑研究仍影响政策等领域，作者曝光后仍发表获资助，建议资助者将作者身份等纳入尽职调查。
AI中文摘要：
当可疑研究进入科学记录后，它与其他文献一样，有机会影响国内外政策、临床指南以及研发活动。本研究利用2022年8月发现的已知作者售卖网络（authorship-for-sale network，简称AFSN），探究可疑研究是否会影响政策、专利和临床指南，以及其作者在曝光后是否仍能获得资助并发表论文。在我们的数据集包含的近2000篇出版物中，有57篇被政策文件引用，12篇被临床指南引用，480篇被专利引用。这些出版物中近四分之一与一项或多项资助相关，而这些资助本可用于支持严谨、符合伦理的科学研究。在我们追踪的278位作者中，PNN（作者售卖网络的简称）于2022年公开曝光后，其发表论文和获得资助的情况仍在继续：超过90%的作者仍在发表论文，23%的作者仍与资助项目相关。我们的研究结果表明，论文工厂（paper mills）和作者售卖网络的参与者，即便其背后的可疑行为被曝光，仍能为政策和临床实践提供信息，并支持其作者的职业发展。我们提出，资助者应将作者身份及网络结构作为整体尽职调查流程的一部分，与引用量、关注度数据等指标一同考量。
英文摘要：
When questionable research has entered the scientific record, it stands the same chance as other literature of shaping national and international policy, clinical guidance, and research and development activities. This study uses a known authorship-for-sale network identified in August 2022 to examine whether questionable research influences policy, patents, and clinical guidelines, and whether its authors continue to secure funding and publish after exposure. Across nearly 2,000 publications in our dataset, 57 were cited in policy documents, 12 in clinical guidelines, and 480 in patents. Nearly a quarter of these publications are linked to one or more grants: funding that could otherwise have supported rigorous, ethical science. Among the 278 authors we traced, publishing and funding continued well after the PNN's public exposure in 2022: over 90% continued publishing, and 23% were linked to a grant. As our findings show, the participants of paper mills and authorship-for-sale networks can still inform policy and clinical practice and support their authors' career progression even after the questionable practices behind them are exposed. We present a case for funders to consider authorship and network structure as part of a holistic due diligence process, alongside metrics such as citation counts and attention data.
