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Navigating Global Trends: A Longitudinal Network Analysis of International Student Mobility - Jeanette B. Ruiz, Pablo M. Flores, George A. Barnett, 2025 This paper analyzes the evolution of the international student mobility (ISM) network from 2000 to 2022 using UNESCO data for 214 countries and territories. It ...

As geopolitical tensions rise and countries suggest caps on international students, these patterns will only accelerate. The competition for international talent is being reshaped in real time, and adaptability will define who can attract them.

🔗Open access: journals.sagepub.com/doi/10.1177/...

- Global events leave fingerprints on mobility. The 2008 financial crisis, immigration policies, and COVID-19 each triggered structural shifts.

- The system is becoming multipolar. Emerging hubs in Asia, Latin America, and the Middle East are challenging the Western-centric model.

We found that:
- The network grew significantly denser, from 28% to 43% connectivity. Regional clusters expanded from six to eight, reflecting greater diversification of destinations.

🎓 New paper out with Jeanette B Ruiz and George Barnett in JSIE (@jsinted.bsky.social): Navigating Global Trends: A Longitudinal Network Analysis of International Student Mobility

We analyzed UNESCO data across 214 countries (2000-2022) to map how the global student mobility network has evolved

Figure 2. 2022 international student mobility network.

Navigating global trends: a longitudinal network analysis of international student mobility

Jeanette B. Ruiz, @pablomflores.phd and George A. Barnett

Open access → doi.org/10.1177/1028...

#StudentMobility #InternationalStudents #IntlEd #NetworkAnalysis

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Actualizado!

We also detected the existence of spirals of "bad" recommendations (down the rabbit hole). Yet, the likelihood of spirals of "good" recommendations is even larger than the bad ones.
If interested, you can take a look at the full paper available for open access.
#commsky #csssky #algorithmaudits

After analyzing 151 algorithm audits from 33 studies, we found that roughly 8-10% of algorithmic recommendations are "bad" while about a quarter actively protect users from self-induced harms ("do good"). This average is consistent across audits irrespective of the platform or the kind of risk.

🚨New Publication🚨
Building on the ideas of risk-utility assessments, we conducted a meta-review to perform a wide-ranging evaluation of the frequency algorithms provide "good" and "bad" recommendations. www.sciencedirect.com/science/article/pii/S026...