Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The DaCapo benchmarks
20061.1k citationsStephen M. Blackburn, Robin Garner et al.ANU Open Research (Australian National University)profile →
Citations per year, relative to Stephen M. Blackburn Stephen M. Blackburn (= 1×)
peers
Martin Hirzel
Countries citing papers authored by Stephen M. Blackburn
Since
Specialization
Citations
This map shows the geographic impact of Stephen M. Blackburn's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stephen M. Blackburn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen M. Blackburn more than expected).
Fields of papers citing papers by Stephen M. Blackburn
This network shows the impact of papers produced by Stephen M. Blackburn. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stephen M. Blackburn. The network helps show where Stephen M. Blackburn may publish in the future.
Co-authorship network of co-authors of Stephen M. Blackburn
This figure shows the co-authorship network connecting the top 25 collaborators of Stephen M. Blackburn.
A scholar is included among the top collaborators of Stephen M. Blackburn based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Stephen M. Blackburn. Stephen M. Blackburn is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Wang, Kunshan, Stephen M. Blackburn, Antony L. Hosking, & Michael Norrish. (2018). Hop, Skip, & Jump. ACM SIGPLAN Notices. 53(3). 1–16.2 indexed citations
2.
Yang, Xi, Stephen M. Blackburn, & Kathryn S. McKinley. (2015). Computer performance microscopy with S him. ANU Open Research (Australian National University). 170–184.16 indexed citations
Esmaeilzadeh, Hadi, Ting Cao, Xi Yang, Stephen M. Blackburn, & Kathryn S. McKinley. (2012). Looking back and looking forward. Communications of the ACM. 55(7). 105–114.24 indexed citations
5.
Yang, Xi, Stephen M. Blackburn, Daniel Frampton, Jennifer B. Sartor, & Kathryn S. McKinley. (2011). Why nothing matters. ACM SIGPLAN Notices. 46(10). 307–324.10 indexed citations
6.
Sartor, Jennifer B., Stephen M. Blackburn, Daniel Frampton, Martin Hirzel, & Kathryn S. McKinley. (2010). Z-rays. ANU Open Research (Australian National University). 471–482.16 indexed citations
Blackburn, Stephen M., Robin Garner, Kathryn S. McKinley, et al.. (2006). The DaCapo benchmarks. ANU Open Research (Australian National University). 169–190.1139 indexed citations breakdown →
10.
Blackburn, Stephen M., Robin Garner, Kathryn S. McKinley, et al.. (2006). The DaCapo benchmarks. ACM SIGPLAN Notices. 41(10). 169–190.164 indexed citations
Blackburn, Stephen M., et al.. (1999). The Transactional Object Cache: A Foundation for High Performance Persistent System Construction. 37–50.1 indexed citations
19.
Blackburn, Stephen M., et al.. (1999). Java Finalize Method, Orthogonal Persistence and Transactions. 363–369.
20.
Blackburn, Stephen M.. (1992). EXTRACTION OF COLOR REGION BOUNDARIES. Machine Vision and Applications. 129(9). 63–66.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.