Rhodri Leng is a quantitative sociologist of science. His research concerns developing understanding of how the structure of citation networks affects the spread of information across the scientific literature, how the spread of information affects the development of scientific opinion, and involves integrating this understanding into theories of scientific development. An important element of his work involves developing methods to detect citation bias and other citation distortions in the literature. This involves methods of systematic literature retrieval, systematic review and meta-analysis, citation network analysis, and content analysis.
Before returning to academia, he held two professional positions in UK politics working as a parliamentary assistant to Mark Lazarowicz MP (House of Commons) and a press and policy officer to Sarah Boyack MSP (Scottish Parliament). He holds MA (1st class Honours) in Politics from the University of Glasgow, a postgraduate MA in Political and Legal Philosophy from the University of York, and MSc(R) and PhD in Science and Technology Studies from the University of Edinburgh.
His PhD was funded by the ESRC Advanced Quantitative Methods (AQM) Studentship and focussed on understanding the role of selective citation in the scientific literature examining the relationship between diet and coronary heart disease. He is currently ESRC Postdoctoral Fellow working on extending his project by developing and refining methods for the detection and evaluation of selective citation in the scientific literature under the mentorship of Dr Niki Vermeulen. During his PhD, he co-authored a book, The Matter of Facts (MIT Press), which outlines and contextualises problems in contemporary science.
Since beginning his PhD in 2016, Rhodri has worked as a tutor on three undergraduate courses (Understanding Public Policy; Social Policy and Society; Evidence, Policy, and Politics), a guest lecturer (Controversies in Medicine, Technology, and Environment; Digesting Food Policy), and developed an online course in systematic literature search and network analysis for literature review for the Biomedical Sciences at Edinburgh. In 2019, he was awarded the ‘Best Student Who Tutors Award’ at the EUSA Teaching Awards.
Sociology of Science scientometrics network science Computational Social Science Public policy History of science
Rhodri’s research focusses on how science develops: how its findings spread and influence opinion, and the biases that undermine this process. It thus relates directly to research integrity and to research impact. His approach is data-driven, requiring the analysis of large bibliographic datasets to study the dynamics of publication and citation. These datasets, in capturing the citation links between documents across the history of science, capture dynamics of scientific development and enable the impact of particular findings, particular innovations – and particular ideas and particular scientists – to be mapped and evaluated.
His research is particularly concerned with recognising citation bias and other citation distortions in the scientific literature, understanding how they arise, and understanding their consequences. This involves methods of systematic literature retrieval, systematic review and meta-analysis, quantitative bibliographic data analysis, citation network analysis, and content analysis. His work draws from models in network science that concern generative mechanisms of topology of dynamic networks to understand how the structure of citation networks affects the spread of information, and hence shapes scientific opinion. It also involves integrating this understanding into sociological and philosophical theories of scientific development and citation behaviour. To date, this has also involved investigating case studies in the biomedical literature – from dietary epidemiology to physiology.
ESRC Postdoctoral Fellow (2020-2021). Project title: Selective citation and the shaping of scientific knowledge: Citation network analysis and the diet–heart debate.
ESRC Advanced Quanitative Methods PhD Studentship (2016-2019): Project title: Selective citation and the shaping of scientific knowledge: Citation network analysis and the diet–heart debate.
Guidance and Feedback Hours
Wednesday 10:00 - 12:000
I am happy to supervise undergraduate or postgraduate students interested in the sociology and history of science, particularly those interested in practices of evidence use in the biomedical literature or those interested in using network analysis. I'm also happy to supervise projects in politics and social policy, particularly those interested in projects exploring the use of evidence in policy.
Find out more about the programmes that I am involved with:
Leng, G., Leng, RI. (2020) The Matter of Facts: Skepticism, Persuasion, and Evidence in Science. MIT Press: https://mitpress.mit.edu/books/matter-facts
Peer Reviewed Journal Articles
Leng, G. , Leng, R. I. and Maclean, S. (2019). The vasopressin−memory hypothesis: a citation network analysis of a debate. Ann. N.Y. Acad. Sci.. doi:10.1111/nyas.14110
Leng, R.I. (2018). A network analysis of the propagation of evidence regarding the effectiveness of fat-controlled diets in the secondary prevention of coronary heart disease (CHD): Selective citation in reviews. PLOS ONE 13(5): e0197716. https://doi.org/10.1371/journal.pone.0197716
Wong, M. and Leng, R. (2019). On the design of linked datasets mapping networks of collaboration in the genomic sequencing of Saccharomyces cerevisiae, Homo sapiens, and Sus scrofa [version 1; peer review: 1 approved]. F1000Research 2019, 8:1200
Mark, W., Leng, R., Viry, G.; Liscovsky. R.B., Miguel, G. (2018). Human, yeast and pig genomics: sequence submissions and first sequence descriptions in the literature (1980-2015) [dataset]. Science, Technology and Innovation Studies. University of Edinburgh. http://dx.doi.org/10.7488/ds/2358
Leng RI., Leng, G. (2020). Unintended Consequences: The Perils of Publication and Citation Bias. The MIT Press Reader. Stable URL: https://thereader.mitpress.mit.edu/perils-of-publication-and-citation-bias/