Robust predictive analysis of international mobility among research talents

Abstract

International organizations, policymakers and researchers have taken notice of the many facets of international mobility and its impact on both the countries of origin and destination. International mobility of research talent has become a critical concern for a developing nation like Ghana, where the departure of highly educated people, particularly in academia, poses significant barriers to national prosperity and the sustainability of the knowledge system. This study employs data on research publications and machine learning techniques to develop a robust system for assessing and forecasting the migration tendencies of Ghanaian researchers to other countries. A longitudinal dataset comprising research disciplines, connections, and mobility trends was created by extracting bibliometric data from Web of Science and Scopus covering the years 2007–2020. Using metrics like support, confidence, lift, and conviction to assess the strength and predictability of emigration rules, the study uses data mining techniques to generate association rules of the field of disciplines to uncover important discipline-destination migration patterns. Time series modeling was used to estimate future migration trends across academic disciplines, revealing that the most vulnerable fields to long-term talent loss are the social sciences, agricultural and biological sciences, and health sciences. The study’s conclusions demonstrate the urgency of data-driven policy initiatives targeted at diaspora participation, institutional building, and talent retention. The suggested paradigm improves knowledge of academic mobility in the Ghanaian context and offers a reproducible strategy for other countries handling the transfer of research talent. The study emphasizes how crucial predictive analytics are to national research systems’ strategy human resource management decision-making.