In modern times, generational artificial intelligence is used in several industries and by many people. One use case that can be considered important but somewhat redundant is the act of searching for related work and other references to cite. As an avenue to better ascertain the value of citations and their corresponding locations, we focus on the common “related work” section as a focus of experimentation with the overall objective to generate the section. In this article, we present a corpus with 400k annotations of that distinguish related work from the rest of the references. Additionally, we show that for the papers in our experiments, the related work section represents the paper just as good, and in many cases, better than the rest of the references. We show that this is the case for more than 74% of the articles when using cosine similarity to measure the distance between two common graph neural network algorithms: Prone and Specter.