破解双盲审查:使用深度学习进行作者归因
Leonard Bauersfeld1, Angel Romero1, Manasi Muglikar1
1Robotics and Perception Group, University of Zurich, Zurich, Switzerland.
PloS one
|June 30, 2023
概括
研究人员开发了一个神经网络,使用文本和文献资料来识别手稿作者. 这种人工智能模型在归因论文时达到高达73%的准确性,提高了科学完整性.
科学领域:
- 计算语言学 计算语言学
- 图书统计学 图书统计学
- 科学出版科学出版
背景情况:
- 双盲同行评审旨在提供公正的评价,但可能会受到作者认可的损害.
- 经验丰富的研究人员往往可以推断出作者身份,从而在同行评审过程中引入了偏见.
研究的目的:
- 开发和评估一种新的计算方法,用于科学手稿的自动作者归因.
- 评估使用文本内容和文献信息来识别匿名研究论文作者的可行性.
主要方法:
- 一个基于变压器的神经网络架构被设计用于作者归因.
- 迄今为止最大的作者识别数据集是使用超过200万个arXiv论文创建的.
- 该模型在arXiv数据的子集上进行了训练和评估,其中最多有2,000名作者.
主要成果:
- 提出的方法在特定的arXiv子集中实现了高达73%的作者归因准确性.
- 一个扩展分析表明了该方法对具有增加计算资源的更大数据集的潜力.
- 该研究确定了关键的文本和文献特征,有助于归因准确性.
结论:
- 使用神经网络的自动作者归属是可行的和有效的.
- 这项技术可以通过识别潜在的作者识别来潜在地减轻科学同行评审中的偏见.
- 提供开源工具以促进复制和进一步研究作者归因.
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