Triangulating Mixed Methods to Access Trends and Impacts in the Journal of International Economic Law

Sahar Behpour, Sarah E. Ryan, Ravi Varma Kumar Bevara, Crystal Bonds and Ting Xiao

Chapter 14 in Navigating Inequities and Social Justice in the Age of Artificial Intelligence, 2026, pp 257-282 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Information science researchers should embrace mixed methods for analyzing trends in journals and other content. This study advances mixed methods trend analysis by employing a triangulated approach using data science and content analysis. We apply a time-biased trend-detection model based on spectral clustering techniques to the Journal of International Economic Law (JIEL). In 687 articles, we uncover topical trends such as the World Trade Organization (WTO) settlement processes. We detect thematic clusters and the predominance of single-authored articles. We then employ qualitative close reading and content analysis of 45 articles using a 19-item Qualtrics tool. This analysis, of a similar time commitment as the quantitative analysis, reveals that most JIEL articles are written by law faculty, use legal analysis as their research method, and contain minimal statistical content. We offer policy recommendations to JIEL and other law review editors. Our findings demonstrate the power of mixed methods research for trend analysis.

Keywords: Business; Artificial Intelligence; Knowledge Management; Social Justice; Leadership; Decision Making; Data Analysis (search for similar items in EconPapers)
JEL-codes: D63 O33 (search for similar items in EconPapers)
Date: 2026
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