Scholarly migration within Mexico: Analyzing internal migration among researchers using Scopus longitudinal bibliometric data
April 14, 2020 Β· Declared Dead Β· π EPJ Data Science
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Andrea Miranda-GonzΓ‘lez, Samin Aref, Tom Theile, Emilio Zagheni
arXiv ID
2004.06539
Category
cs.DL: Digital Libraries
Cross-listed
cs.SI,
physics.soc-ph
Citations
31
Venue
EPJ Data Science
Last Checked
2 months ago
Abstract
The migration of scholars is a major driver of innovation and of diffusion of knowledge. Although large-scale bibliometric data have been used to measure international migration of scholars, our understanding of internal migration among researchers is very limited. This is partly due to a lack of data aggregated at a suitable sub-national level. In this study, we analyze internal migration in Mexico based on over 1.1 million authorship records from the Scopus database. We trace the movements of scholars between Mexican states, and provide key demographic measures of internal migration for the 1996-2018 period. From a methodological perspective, we develop a new framework for enhancing data quality, inferring states from affiliations, and detecting moves from modal states for the purposes of studying internal migration among researchers. Substantively, we combine demographic and network science techniques to improve our understanding of internal migration patterns within country boundaries. The migration patterns between states in Mexico appear to be heterogeneous in size and direction across regions. However, while many scholars remain in their regions, there seems to be a preference for Mexico City and the surrounding states as migration destinations. We observed that over the past two decades, there has been a general decreasing trend in the crude migration intensity. However, the migration network has become more dense and more diverse, and has included greater exchanges between states along the Gulf and the Pacific Coast. Our analysis, which is mostly empirical in nature, lays the foundations for testing and developing theories that can rely on the analytical framework developed by migration scholars, and the richness of appropriately processed bibliometric data.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Digital Libraries
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Measuring academic influence: Not all citations are equal
R.I.P.
π»
Ghosted
The Open Access Advantage Considering Citation, Article Usage and Social Media Attention
R.I.P.
π»
Ghosted
A Bibliometric Review of Large Language Models Research from 2017 to 2023
R.I.P.
π»
Ghosted
On the Performance of Hybrid Search Strategies for Systematic Literature Reviews in Software Engineering
R.I.P.
π»
Ghosted
A Systematic Identification and Analysis of Scientists on Twitter
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted