预测撤回的研究:一个数据集和机器学习方法
Aaron H A Fletcher1, Mark Stevenson2
1School of Computer Science, The University of Sheffield, Regent Court, Sheffield, S1 4DP, UK. ahafletcher1@sheffield.ac.uk.
Research integrity and peer review
|June 10, 2025
概括
机器学习模型可以帮助识别撤回的科学文章,提高研究完整性. 这项研究开发了一个数据集和评估模型,发现传统分类器和Llama 3.2在预测收缩方面具有竞争力.
科学领域:
- 图书统计学 图书统计学
- 科学出版业的科学出版.
- 机器学习是机器学习.
背景情况:
- 收回的科学文章损害了研究的完整性,并可能延续错误信息.
- 开发识别撤回的方法对于保持可靠的科学记录至关重要.
研究的目的:
- 创建一个全面的数据集的收回的文章与文献资料元数据.
- 训练和评估机器学习 (ML) 模型,用于预测文章撤回.
- 通过废弃性研究评估ML分类器中的特征重要性.
主要方法:
- 通过整合Retraction Watch和OpenAlex数据构建了一个开放访问数据集.
- 一个经过案例控制的设计将收缩的物品与非收缩的对应物品配对.
- 包括传统分类器和语言模型在内的各种ML模型被训练并使用准确性,精度,回忆和F1分数进行评估.
主要成果:
- 拉玛3.2基本模型的整体准确性很高.
- 随机森林在未收缩的物品中获得了0.687的精度;拉玛3.2在收缩的物品中获得了0.683的精度.
- 传统的ML分类器通常表现优于语境语言模型,Llama 3.2显示出竞争性表现.
结论:
- 机器学习有效地帮助识别撤回的研究,尽管没有一个单一的模型主导了所有指标.
- 这些发现支持出版商和审稿人开发自动化工具,以检测有问题的出版物.
- 需要进一步的研究来完善模型,并纳入额外的功能,以提高预测准确度.
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