Cite-seeing与评审：同行评审中的引用偏差研究
Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review
AI总结：
本研究针对同行评审中是否存在引用偏差的问题，结合两大领域旗舰会议的评审流程开展观察性研究，控制混杂因素并采用多种建模方法，证实引用审稿人工作会提升投稿评分，明确了该偏差的效应量。
摘要：
引用作为科学影响力评估的关键因素，在研究者的职业生涯中发挥着重要作用。许多坊间经验建议作者利用这一点，引用潜在审稿人的工作，以期让自己的投稿获得更正面的评价。在本研究中，我们调查了此类引用偏差是否真实存在：投稿中引用审稿人自身的工作，是否会导致审稿人对该投稿产生正向偏差？我们结合机器学习与算法经济学领域两大旗舰会议的评审流程，开展了一项观察性研究，以检验同行评审中的引用偏差。在分析中，我们仔细控制了论文质量、审稿人专业度等各类混杂因素，并采用多种建模技术来缓解模型不匹配带来的疑虑。总体而言，我们的分析涵盖1314篇论文和1717名审稿人，在我们研究的两个会议中均检测到了引用偏差。在效应量方面，通过引用审稿人的工作，投稿有相当大的概率从该审稿人处获得更高的评分：在5分制李克特量表中，评分预计提升约0.23分。作为参考，单名审稿人的评分每提高1分，投稿的排名平均提升11%。
英文摘要：
Citations play an important role in researchers' careers as a key factor in evaluation of scientific impact. Many anecdotes advice authors to exploit this fact and cite prospective reviewers to try obtaining a more positive evaluation for their submission. In this work, we investigate if such a citation bias actually exists: Does the citation of a reviewer's own work in a submission cause them to be positively biased towards the submission? In conjunction with the review process of two flagship conferences in machine learning and algorithmic economics, we execute an observational study to test for citation bias in peer review. In our analysis, we carefully account for various confounding factors such as paper quality and reviewer expertise, and apply different modeling techniques to alleviate concerns regarding the model mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and detects citation bias in both venues we consider. In terms of the effect size, by citing a reviewer's work, a submission has a non-trivial chance of getting a higher score from the reviewer: an expected increase in the score is approximately 0.23 on a 5-point Likert item. For reference, a one-point increase of a score by a single reviewer improves the position of a submission by 11% on average.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
