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

arXiv: https://arxiv.org/abs/2104.04580 · HTML: https://mlsanity.com/paper/2104.04580/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 47 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Built on by (most cited first, 4 of 4)

- [A Synthetic Prediction Market for Estimating Confidence in Published Work](https://mlsanity.com/paper/2201.06924/) (2021)
- [Can citations tell us about a paper's reproducibility? A case study of machine learning papers](https://mlsanity.com/paper/2405.03977/) (2024)
- [A prototype hybrid prediction market for estimating replicability of published work](https://mlsanity.com/paper/2303.00866/) (2023)
- [Human-AI Collaboration for Estimating Scientific Replicability](https://mlsanity.com/paper/2605.27394/) (2026)

## Abstract (arXiv)

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.