Recent work in the information sciences, especially informetrics and scientometrics, has made substantial contributions to the development of new metrics that eschew the intrinsic biases of citation metrics. This work has tended to employ either network scientific (topological) approaches to quantifying the disruptiveness of peer-reviewed research, or topic modeling approaches to quantifying conceptual novelty. We propose a combination of these approaches, investigating the prospect of topological data analysis (TDA), specifically persistent homology and mixup barcodes, as a means of understanding the negative space among document embeddings generated by topic models. Using top2vec, we embed documents and topics in n-dimensional space, we use persistent homology to identify holes in the embedding distribution, and then use mixup barcodes to determine which holes are being filled by a set of unobserved publications. In this case, the unobserved publications represent research that was published before or after the data used to train top2vec. We investigate the extent that negative embedding space represents missing context (older research) versus innovation space (newer research), and the extend that the documents that occupy this space represents integrations of the research topics on the periphery. Potential applications for this metric are discussed.