谁为开放评审买单？可见的作者声誉及其对评分的影响
Who Pays for Open Review? Visible Author Reputation and Its Effect on Ratings
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中文总结 AI 辅助
本研究通过分析ICLR 2026的18,000多份投稿及AI评审模拟，发现开放评审下作者声誉对评分的影响更显著，且差异集中在临界评分，揭示了开放评审可能带来的声誉偏差。
中文摘要 AI 辅助
2025年11月的一个OpenReview漏洞破坏了多个会议的匿名性，并引发了开放评审的呼声，这促使我们思考从盲审转向开放评审对作者意味着什么。通过分析ICLR 2026的超过18,000份已评审投稿，根据arXiv预印本时间将其分为事实上的开放组和盲审组，我们发现在这两种机制下，评分都随作者声誉上升而提高，其中开放评审下的斜率更陡且具有统计显著性，并且开放与盲审之间的差异集中在临界评分上。该模式在五个声誉代理指标（包括机构、h指数和引用次数）、三种作者聚合规则和五种开放窗口定义中均保持一致。一项受控模拟实验使用五个AI模型作为评审者，固定稿件不变而改变作者声誉，重现了该效应。例如，当评审者为claude-opus-5时，作者从低声誉变为高声誉，评分上升0.5分。
英文摘要
An OpenReview bug in November 2025 broke anonymity at several conferences and prompted calls for open review, which motivate us to ask what shifting from blind to open would mean for authors. Analyzing over 18,000 reviewed submissions to ICLR 2026, split into de facto open and blind groups by arXiv preprint timing, we find that ratings rise with author reputation under both mechanisms, with a steeper slope under open review that is statistically significant, and that the open-blind difference is concentrated at the borderline ratings. The pattern holds across five reputation proxies (including institution, h-index, and citation count), three author-aggregation rules, and five definitions of the open window. A controlled simulation with five AI models as reviewers, holding the manuscript fixed and varying the author reputation, reproduces the effect. With claude-opus-5 as the reviewer, for example, rating rises by 0.5 points as the author moves from low to high reputation.
发表机构
- Hefei University（合肥学院）
- Pengcheng Laboratory（鹏城实验室）
- Lanzhou University（兰州大学）
机构由 AI 辅助整理，请以论文原文为准。
