The citation-data of the articles was treated like the words in astandard computational text classification problem: For themap-layout, citation-vectors were reduced with singular valuedecomposition (SVD) to remove noise. This data was in turn transformedwith uniform manifold approximation and projection (UMAP) into a twodimensional map, which will not only be presented as a poster, but ismade available in interactive online form,(<ref xmlns="http://www.tei-c.org/ns/1.0" target="https://homepage.univie.ac.at/noichlm94/full/zoom_final/index.html">https://homepage.univie.ac.at/noichlm94/full/zoom_final/index.html</ref>) of which the graphic below ought to give an idea. Each dot of the mapping represents a paper, which is positioned relative to all other papers according to similarities in the sources it cites. The clustering was produced using hDBSCAN5 on a 30-dimensional UMAP-embedding of the data. Clusters can be interpreted as groups of scholarly co-engagement. The clusters were identified using the most frequent words from the abstracts of the papers, the most cited sources, and the most common venues of publication.