\section{Conclusion}

This study attempts to shed more light on how the practice of crowdsourcing linguistic data interacts with model training in the challenging field of IDRR. 
Specifically, we analyze whether dataset cartography \cite{swayamdipta_dataset_2020} can help identify errors and interpretations with the help of annotator-aware architectures \cite{frenda2024perspectivist}. We find that error identification based on low confidence is infeasible for IDRR datasets; however, high confidence in models coincides with strong heuristics and with high item-wise agreement, pointing at shortcut usage by both models and crowd workers. Likely stemming from the crowd workers' limited training, this case needs to be treated explicitly to ensure robust model training.