Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
This work addresses the resource-intensive challenge of verifying research reproducibility for social and behavioral scientists, but it is incremental as it builds on existing methods for feature extraction and prediction.
The paper tackles the problem of predicting the reproducibility of social and behavioral science papers by developing a machine learning framework that extracts features like bibliometric, statistical, and semantic data, identifying 9 key features that improve prediction accuracy, though no specific performance numbers are provided.
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 resource-intensive 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 reproducibility of published research claims. Bibliometric features, venue features, and author features are collected from public APIs or extracted using open source machine learning libraries with customized parsers. Statistical features, such as p-values, are extracted by recognizing patterns in the body text. Semantic features, such as funding information, are obtained from public APIs or are extracted using natural language processing models. We analyze pairwise correlations between individual features and their importance for predicting a set of human-assessed ground truth labels. In doing so, we identify a subset of 9 top features that play relatively more important roles in predicting the reproducibility of SBS papers in our corpus. Results are verified by comparing performances of 10 supervised predictive classifiers trained on different sets of features.