Automated reproducibility assessments in the social and behavioral sciences using large language models

Reproducibility assessments in social and behavioral sciences can now be automated using large language models, significantly reducing the resources required for evaluating published findings. By leveraging these models, researchers can reanalyze original data from published studies to verify the accuracy of the results, a process that was previously labor-intensive and difficult to scale. A recent study demonstrated the effectiveness of this approach by using large language models to assess the reproducibility of 76 published studies¹. This development has significant implications for the field, as it enables researchers to efficiently evaluate the validity of published research and identify potential flaws or biases. The ability to automate reproducibility assessments can also help to increase the transparency and reliability of research in the social and behavioral sciences, which is critical for informing policy and practice decisions. This matters to practitioners because it can help to build trust in research findings and ensure that decisions are based on accurate and reliable information.