GPSC

This symposium explores the transformative impact of scientific mobility policies on scientific collaboration networks and the flow of knowledge. Using cutting-edge techniques in network science and machine learning, we aim to uncover how these initiatives shape global research dynamics and influence long-term scientific development. Many countries, specifically developing nations, often face significant hurdles in enhancing their scientific output, including brain drain and budget limitations. In response, several countries—such as China, India, Indonesia, and Brazil—have introduced scientific mobility policies to provide researchers with international exposure and improve scientific outcomes.

Examples include:

China Scholarship Council: Funding for PhD programs and short-term visits to foreign institutes.
Indonesian Government Programs: Sponsorships for PhD students to study abroad and return to their home country.
India’s SPARC Fellowship: Initiatives fostering foreign collaborations and bilateral exchanges.
Brazil’s visiting researcher program

This symposium brings together researchers, policymakers, and practitioners to discuss the effectiveness of these initiatives, their impact on knowledge exchange, and the challenges in ensuring equitable scientific progress. Through interdisciplinary dialogue and empirical analyses, we seek to identify best practices and develop strategies to optimize scientific mobility for sustainable global research development.

Our Goals

This workshop focuses on modelling and assessing the long-term impacts of such scientific mobility policies, comparing countries adopting these policies with those not. Key objectives include:

Analyzing co-authorship networks and global collaboration dynamics.
Understanding shifts in research topics and collaboration patterns.
Evaluating the structural and dynamic changes in scientific networks over time.
Assessing the long-term effects on a country’s scientific development and individual researchers.
Understanding how scientific mobility and return policies affect collaboration patterns.
Understanding disparities and biases in scientific collaboration.

This research proposal seeks to integrate statistical analyses, network science, machine learning, and policy analysis to enhance our understanding of the effects and implications of academic mobility programs and policies. By employing these interdisciplinary approaches, we aim to investigate the influence of such programs on academic opportunities and outcomes, thereby contributing to more effective policy formulation and implementation.Join us to delve into these critical questions and contribute to shaping the future of scientific collaboration!