IJCNN 2025 review流程优化，用评分校准减少审稿人偏见
The IJCNN 2025 Review Process
- 引入评分指数与校准版本，降低审稿人个体差异影响
- 2025年投稿量达5526篇，审稿人超7877人，接受率约39%
- 适合关注学术会议评审机制改进的研究者参考
国际神经网络联合会议（IJCNN）是神经网络理论、分析与应用领域的顶级国际会议。2025年会议共收到5,526篇论文投稿，有7,877名活跃审稿人、426名领域主席和超过2,300名参会者，相较上届投稿量增长约100%，审稿人数量增长200%，参会人数增长超50%。本文介绍了评审过程中的若干关键环节，包括通过评分指数评估审稿人打分，并实验性采用校准版本以消除审稿人个体偏差。
原文摘要 · Abstract (English)
The International Joint Conference on Neural Networks (IJCNN) is the premier international conference in the area of neural networks theory, analysis, and applications. The 2025 edition of the conference comprised 5,526 paper submissions, 7,877 active reviewers, 426 area chairs, 2,152 accepted papers, and more than 2,300 attendees. This represents a growth of about 100% in terms of submissions, 200% in terms of reviewers, and over 50% in terms of attendees as compared to the previous edition. In this paper, we describe several key aspects of the whole review process, including a strategy for ranking the scores provided by the reviewers by evaluating a score index and a calibrated version used experimentally to remove reviewer-specific bias from reviews.
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