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.
Statistical physics of human cooperation
20171.1k citationsMatjaž Perc, Jillian Jordan et al.Physics Reportsprofile →
Third-party punishment as a costly signal of trustworthiness
2016301 citationsJillian Jordan, Moshe Hoffman et al.Natureprofile →
Don’t get it or don’t spread it: comparing self-interested versus prosocial motivations for COVID-19 prevention behaviors
2021175 citationsJillian Jordan, Erez Yoeli et al.Scientific Reportsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Jillian Jordan
Since
Specialization
Citations
This map shows the geographic impact of Jillian Jordan'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 Jillian Jordan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jillian Jordan more than expected).
This network shows the impact of papers produced by Jillian Jordan. 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 Jillian Jordan. The network helps show where Jillian Jordan may publish in the future.
Co-authorship network of co-authors of Jillian Jordan
This figure shows the co-authorship network connecting the top 25 collaborators of Jillian Jordan.
A scholar is included among the top collaborators of Jillian Jordan 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 Jillian Jordan. Jillian Jordan is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jordan, Jillian, Erez Yoeli, & David G. Rand. (2021). Don’t get it or don’t spread it: comparing self-interested versus prosocial motivations for COVID-19 prevention behaviors. Scientific Reports. 11(1). 20222–20222.175 indexed citations breakdown →
Perc, Matjaž, Jillian Jordan, David G. Rand, et al.. (2017). Statistical physics of human cooperation. Physics Reports. 687. 1–51.1132 indexed citations breakdown →
Perc, Matjaž, Jillian Jordan, David G. Rand, et al.. (2017). Statistical physics of human cooperation. Repository of the Academy's Library (Library of the Hungarian Academy of Sciences).4 indexed citations
Jordan, Jillian & David G. Rand. (2016). Building Costly Signaling from the Ground Up: A Model of Third-Party Punishment as a Costly Signal of Exposure to Repeated Interactions. SSRN Electronic Journal.1 indexed citations
14.
Jordan, Jillian, Moshe Hoffman, Paul Bloom, & David G. Rand. (2016). Third-party punishment as a costly signal of trustworthiness. Nature. 530(7591). 473–476.301 indexed citations breakdown →
15.
Jordan, Jillian, Alexander Peysakhovich, & David G. Rand. (2015). Why We Cooperate. SSRN Electronic Journal.5 indexed citations
Capraro, Valerio, Jillian Jordan, & David G. Rand. (2014). Cooperation increases with the benefit-to-cost ratio in one-shot Prisoner's Dilemma experiments.. arXiv (Cornell University).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.