Automatically Finding and Categorizing Replication Studies
This addresses the issue for researchers and practitioners who rely on accurate citations, though it is incremental as it builds on existing text analysis methods.
The paper tackled the problem of poor discoverability of replication studies in experimental science by developing a system to automatically find and categorize them, achieving an AUROC of 0.886 for identifying replication studies and 0.664 for distinguishing successful from failed ones.
In many fields of experimental science, papers that failed to replicate continue to be cited as a result of the poor discoverability of replication studies. As a first step to creating a system that automatically finds replication studies for a given paper, 334 replication studies and 344 replicated studies were collected. Replication studies could be identified in the dataset based on text content at a higher rate than chance (AUROC = 0.886). Additionally, successful replication studies could be distinguished from failed replication studies at a higher rate than chance (AUROC = 0.664).