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.
ChatGPT Goes to Law School
2023302 citationsJonathan H. Choi, Kristin E. Hickman et al.SSRN Electronic Journalprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Daniel Schwarcz
Since
Specialization
Citations
This map shows the geographic impact of Daniel Schwarcz'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 Daniel Schwarcz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Schwarcz more than expected).
This network shows the impact of papers produced by Daniel Schwarcz. 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 Daniel Schwarcz. The network helps show where Daniel Schwarcz may publish in the future.
Co-authorship network of co-authors of Daniel Schwarcz
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Schwarcz.
A scholar is included among the top collaborators of Daniel Schwarcz 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 Daniel Schwarcz. Daniel Schwarcz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Choi, Jonathan H., Kristin E. Hickman, Amy Monahan, & Daniel Schwarcz. (2023). ChatGPT Goes to Law School. SSRN Electronic Journal.302 indexed citations breakdown →
Schwarcz, Daniel. (2019). Towards A Civil Rights Approach to Insurance Anti-Discrimination Law. The De Paul law review. 69(2). 15.1 indexed citations
6.
Prince, Anya E. R. & Daniel Schwarcz. (2019). Proxy Discrimination in the Age of Artificial Intelligence and Big Data. Iowa law review. 105(3). 1257–1318.68 indexed citations
7.
Schwarcz, Daniel. (2017). Ending Public Utility Style Rate Regulation in Insurance. Yale journal on regulation. 35(3). 7.2 indexed citations
8.
Schwarcz, Daniel. (2015). The Risks of Shadow Insurance. SSRN Electronic Journal. 50(1). 2504.6 indexed citations
9.
Schwarcz, Daniel. (2015). A Critical Take on Group Regulation of Insurers in the United States. UC Irvine law review. 5(3). 537.2 indexed citations
10.
Schwarcz, Daniel & Steven L. Schwarcz. (2014). Regulating Systemic Risk in Insurance. The University of Chicago Law Review. 81(4). 1569.14 indexed citations
Monahan, Amy & Daniel Schwarcz. (2010). Will Employers Undermine Health Care Reform by Dumping Sick Employees. Virginia Law Review. 97(1). 125–197.8 indexed citations
16.
Schwarcz, Daniel. (2010). Regulating Consumer Demand in Insurance Markets. Data Archiving and Networked Services (DANS). 3(1). 23–45.6 indexed citations
17.
Schwarcz, Daniel. (2009). Redesigning Consumer Dispute Resolution: A Case Study of the British and American Approaches to Insurance Claims Conflict. 83.4 indexed citations
18.
Schwarcz, Daniel. (2009). Differential Compensation and the "Race to the Bottom" in Consumer Insurance Markets. SSRN Electronic Journal. 15.2 indexed citations
19.
Schwarcz, Daniel. (2006). Beyond Disclosure: The Case for Banning Contingent Commissions. SSRN Electronic Journal. 25(2). 3.5 indexed citations
20.
Schwarcz, Daniel. (2006). A Products Liability Theory for the Judicial Regulation of Insurance Policies. SSRN Electronic Journal. 48(4). 1389.2 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.