Galaxy morphology characterisation is an important area of study, as the type and formation of galaxies offer insights into the origin and evolution of the universe. Owing to the increased availability of images and galaxies, scientists have turned crowd-sourcing to automate the process of instance labelling. However, research has shown that using crowd-sourced labels for galaxy classification comes with many pitfalls. An alternative approach to galaxy classification is metric learning. Metric learning allows for improved representations for classification, anomaly detection, information retrieval, clustering and dimensionality reduction. Understanding the implications of this approach regarding crowd-sourced labels is of paramount importance if scientists intend to continue using them.