Warfare by Other Means: China\u27s Economic Lawfare

Lawfare—the use of legal tools to achieve goals that would normally be gained via armed conflict—has become both a frequently used tool among state and non-state actors and has garnered a great deal of scholarly attention. Yet there is one area of lawfare that it is frequently employed but has not attracted a high degree of interest among researchers: economic lawfare. Broadly defined, economic lawfare is the utilization of legal tools connected to economic activity to either harm an adversary’s economic standing or to coerce states into compliance. In recent years, China has become one of the largest utilizers of economic lawfare. This article considers the means, methods, and scope of China’s current lawfare campaign, while also offering predictions on what future economic lawfare tools Beijing might employ. I argue that thus far, China has largely rooted its economic lawfare campaign through a series of restrictions on trade. This is largely due to the fact that China has immense power over international commerce, and also has few other economic lawfare tools at its immediate disposal. In considering China’s future economic lawfare strategies, however, I suggest that Beijing may look beyond mere trade-related tools. As tensions with the West continue to deepen, President Xi Jinping’s government may look to expand its economic lawfare arsenal as a way to counter the U.S. and its allies without resorting to kinetic action. Chinese leadership may, for example, attempt to use more finance-based tools or might begin to wage economic lawfare in the Global South

Runaway Events Dominate the Heavy Tail of Citation Distributions

Statistical distributions with heavy tails are ubiquitous in natural and social phenomena. Since the entries in heavy tail have disproportional significance, the knowledge of its exact shape is very important. Citations of scientific papers form one of the best-known heavy tail distributions. Even in this case there is a considerable debate whether citation distribution follows the log-normal or power-law fit. The goal of our study is to solve this debate by measuring citation distribution for a very large and homogeneous data. We measured citation distribution for 418,438 Physics papers published in 1980-1989 and cited by 2008. While the log-normal fit deviates too strong from the data, the discrete power-law function with the exponent $\gamma=3.15$ does better and fits 99.955% of the data. However, the extreme tail of the distribution deviates upward even from the power-law fit and exhibits a dramatic "runaway" behavior. The onset of the runaway regime is revealed macroscopically as the paper garners 1000-1500 citations, however the microscopic measurements of autocorrelation in citation rates are able to predict this behavior in advance.Comment: 6 pages, 5 Figure

Evaluative Informetrics: The Art of Metrics-Based Research Assessment: Festschrift in Honour of Henk F. Moed

We intend to edit a Festschrift for Henk Moed combining a “best of” collection of his papers and new contributions (original research papers) by authors having worked and collaborated with him. The outcome of this original combination aims to provide an overview of the advancement of the field in the intersection of bibliometrics, informetrics, science studies and research assessment

Relationship among research collaboration, number of documents and number of citations. A case study in Spanish computer science production in 2000-2009.

This paper analyzes the relationship among research collaboration, number of documents and number of citations of computer science research activity. It analyzes the number of documents and citations and how they vary by number of authors. They are also analyzed (according to author set cardinality) under different circumstances, that is, when documents are written in different types of collaboration, when documents are published in different document types, when documents are published in different computer science subdisciplines, and, finally, when documents are published by journals with different impact factor quartiles. To investigate the above relationships, this paper analyzes the publications listed in the Web of Science and produced by active Spanish university professors between 2000 and 2009, working in the computer science field. Analyzing all documents, we show that the highest percentage of documents are published by three authors, whereas single-authored documents account for the lowest percentage. By number of citations, there is no positive association between the author cardinality and citation impact. Statistical tests show that documents written by two authors receive more citations per document and year than documents published by more authors. In contrast, results do not show statistically significant differences between documents published by two authors and one author. The research findings suggest that international collaboration results on average in publications with higher citation rates than national and institutional collaborations. We also find differences regarding citation rates between journals and conferences, across different computer science subdisciplines and journal quartiles as expected. Finally, our impression is that the collaborative level (number of authors per document) will increase in the coming years, and documents published by three or four authors will be the trend in computer science literature

Evolution of priorities in strategic funding for collaborative health research. A comparison of the European Union Framework Programmes to the program funding by the United States National Institutes of Health

The historical research-funding model, based on the curiosity and academic interests of researchers, is giving way to new strategic funding models that seek to meet societal needs. We investigated the impact of this trend on health research funded by the two leading funding bodies worldwide, i.e. the National Institutes of Health (NIH) in the United States, and the framework programs of the European Union (EU). To this end, we performed a quantitative analysis of the content of projects supported through programmatic funding by the EU and NIH, in the period 2008-2014 and 2015-2020. We used machine learning for classification of projects as basic biomedical research, or as more implementation directed clinical therapeutic research, diagnostics research, population research, or policy and management research. In addition, we analyzed funding for major disease areas (cancer, cardio-metabolic and infectious disease). We found that EU collaborative health research projects clearly shifted towards more implementation research. In the US, the recently implemented UM1 program has a similar profile with strong clinical therapeutic research, while other NIH programs remain heavily oriented to basic biomedical research. Funding for cancer research is present across all NIH and EU programs, and in biomedical as well as more implementation directed projects, while infectious diseases is an emerging theme. We conclude that demand for solutions for medical needs leads to expanded funding for implementation- and impact-oriented research. Basic biomedical research remains present in programs driven by scientific initiative and strategies based on excellence, but may be at risk of declining funding opportunities

Long term productivity and collaboration in information science

This is an accepted manuscript of an article published by Springer in Scientometrics on 02/07/2016, available online: https://doi.org/10.1007/s11192-016-2061-8 The accepted version of the publication may differ from the final published version.Funding bodies have tended to encourage collaborative research because it is generally more highly cited than sole author research. But higher mean citation for collaborative articles does not imply collaborative researchers are in general more research productive. This article assesses the extent to which research productivity varies with the number of collaborative partners for long term researchers within three Web of Science subject areas: Information Science & Library Science, Communication and Medical Informatics. When using the whole number counting system, researchers who worked in groups of 2 or 3 were generally the most productive, in terms of producing the most papers and citations. However, when using fractional counting, researchers who worked in groups of 1 or 2 were generally the most productive. The findings need to be interpreted cautiously, however, because authors that produce few academic articles within a field may publish in other fields or leave academia and contribute to society in other ways

Can Amazon.com reviews help to assess the wider impacts of books?

University of Wolverhampto

The 17th International Conference on Scientometrics and Informetrics

[No abstract available