• Corpus ID: 233210295

Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models

@article{Wu2021PredictingTR,
  title={Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models},
  author={Jian Wu and Rajal Nivargi and Sree Sai Teja Lanka and Arjun Manoj Menon and Sai Ajay Modukuri and Nishanth Nakshatri and Xin Wei and Zhuoer Wang and James Caverlee and Sarah Michele Rajtmajer and C. Lee Giles},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.04580}
}
In recent years, significant effort has been invested verifying the reproducibility and robustness of research claims in social and behavioral sciences (SBS), much of which has involved resourceintensive replication projects. In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features. We propose a framework that extracts five types of features from scholarly work that can be used to support assessments of… 

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