Citations per year, relative to Edward K. Cheng Edward K. Cheng (= 1×)
peers
Judy Pearsall
Countries citing papers authored by Edward K. Cheng
Since
Specialization
Citations
This map shows the geographic impact of Edward K. Cheng'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 Edward K. Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward K. Cheng more than expected).
This network shows the impact of papers produced by Edward K. Cheng. 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 Edward K. Cheng. The network helps show where Edward K. Cheng may publish in the future.
Co-authorship network of co-authors of Edward K. Cheng
This figure shows the co-authorship network connecting the top 25 collaborators of Edward K. Cheng.
A scholar is included among the top collaborators of Edward K. Cheng 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 Edward K. Cheng. Edward K. Cheng is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Cheng, Edward K., et al.. (2018). Beyond the Witness: Bringing a Process Perspective to Modern Evidence Law. SSRN Electronic Journal.6 indexed citations
3.
Cheng, Edward K., et al.. (2018). Surprise vs. Probability as a Metric for Proof. Seton Hall Law Review. 48(4). 1081.3 indexed citations
4.
Pardo, Michael S. & Edward K. Cheng. (2015). Accuracy, Optimality, and the Preponderance Standard. eYLS (Yale Law School).9 indexed citations
5.
Cheng, Edward K.. (2013). Being Pragmatic About Forensic Linguistics. eYLS (Yale Law School). 21(2). 12.4 indexed citations
6.
Cheng, Edward K.. (2012). Reconceptualizing the Burden of Proof. The Yale Law Journal. 122(5). 1254.16 indexed citations
7.
Cheng, Edward K.. (2012). When 10 Trials are Better than 1000: An Evidentiary Perspective on Trial Sampling. University of Pennsylvania Law Review. 160(4). 955.2 indexed citations
8.
Cheng, Edward K.. (2010). Scientific Evidence as Foreign Law. Brooklyn law review. 75(4). 4.1 indexed citations
9.
Cheng, Edward K.. (2009). A Practical Solution to the Reference Class Problem. SSRN Electronic Journal.11 indexed citations
10.
Cheng, Edward K.. (2008). The Myth of the Generalist Judge: An Empirical Analysis of Opinion Specialization in the Federal Courts of Appeals. Stanford Law Review.7 indexed citations
11.
Cheng, Edward K.. (2007). The Myth of the Generalist Judge. Stanford Law Review. 61(3). 519.13 indexed citations
12.
Cheng, Edward K.. (2007). Independent judicial research in the Daubert age.. Duke Law Journal. 56(5). 1263–318.
13.
Cheng, Edward K., David L. Faigman, Michael J. Saks, & Joseph Sanders. (2006). Modern scientific evidence : the law and science of expert testimony.196 indexed citations
14.
Cheng, Edward K.. (2006). Should Judges Do Independent Research on Scientific Issues. 90(2). 58.1 indexed citations
15.
Cheng, Edward K.. (2005). Structural Laws and the Puzzle of Regulating Behavior. Northwestern University law review. 100(2). 655.16 indexed citations
16.
Cheng, Edward K.. (2005). Mitochondrial DNA: Emerging Legal Issues. eYLS (Yale Law School). 13(1). 6.4 indexed citations
17.
Cheng, Edward K. & Albert Yoon. (2004). Does Frye or Daubert Matter? A Study of Scientific Admissibility Standards. Virginia Law Review. 91. 471.30 indexed citations
18.
Cheng, Edward K.. (2003). Changing Scientific Evidence. Minnesota law review. 88.
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.