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Abstract This study examines the diversity of institutional human capital at Historically Black Colleges and Universities (HBCUs) by analyzing faculty educational backgrounds using a large data set on faculty hiring and placement. The analysis includes approximately four thousand faculty members employed at 10 research-intensive R2 HBCUs between 2011 and 2020. The results reveal that the 10 R2 HBCUs primarily hired tenure-track faculty from predominantly White R1 institutions. In contrast, HBCUs hired approximately 20% of their own graduates, while less than 10% of hires came from other HBCUs. Regarding placement, about 60% of HBCU graduates sought employment at HBCUs, while only a small number found employment at R1 institutions. Notably, Howard University placed 30 graduates at R1 institutions. This downward placement pattern underscores a significant trend: most HBCU hires are from R1 institutions, while HBCU graduates primarily find employment at institutions with lower research intensity. Understanding these patterns is crucial for addressing disparities in faculty representation and supporting the growth of Black professionals in academia. 

Finding relevant publications is a common task. Typically, a researcher browses through a list of publications and traces additional relevant publications. When relevant publications are identified, the list may be expanded by the citation links of the relevant publications. The information needs of researchers may change as they go through such iterative processes. The exploration process quickly becomes cumbersome as the list expands. Most existing academic search systems tend to be limited in terms of the extent to which searchers can adapt their search as they proceed. In this article, we introduce an adaptive visual exploration system named PaperPoles to support exploration of scientific publications in a context‐aware environment. Searchers can express their information needs by intuitively formulating positive and negative queries. The search results are grouped and displayed in a cluster view, which shows aspects and relevance patterns of the results to support navigation and exploration. We conducted an experiment to compare PaperPoles with a list‐based interface in performing two academic search tasks with different complexity. The results show that PaperPoles can improve the accuracy of searching for the simple and complex tasks. It can also reduce the completion time of searching and improve exploration effectiveness in the complex task. PaperPoles demonstrates a potentially effective workflow for adaptive visual search of complex information.