Dataset
Data for the training section of 'Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models' paper
| Τίτλος: | Data for the training section of 'Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models' paper |
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| Συγγραφείς: | Ghorbanpour, Faeze, Malaguth, Thiago, Akbaritabar, Aliakbar |
| Στοιχεία εκδότη: | Zenodo |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | Zenodo |
| Θεματικοί όροι: | Demography/classification, Machine learning, Natural language processing |
| Περιγραφή: | This repository includes the dataset for the training section of "Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models." Article title: Differentiating Emigration from Return Migration of Scholars Using Name-Based Nationality Detection Models Published in: ICWSM 2025 Manuscript authors: Faeze Ghorbanpour, Thiago Zordan Malaguth, and Aliakbar Akbaritabar Article DOI: https://doi.org/10.1609/icwsm.v19i1.35836 Abstract: Most web and digital trace data do not include information about an individual's nationality due to privacy concerns. The lack of data on nationality can create challenges for migration research. It can lead to a left-censoring issue since we are uncertain about the migrant's country of origin. Once we observe an emigration event, if we know the nationality, we can differentiate it from return migration. We propose methods to detect the nationality with the least available data, i.e., full names. We use the detected nationality in comparison with the country of academic origin, which is a common approach in studying the migration of researchers. We gathered 2.6 million unique name-nationality pairs from Wikipedia and categorized them into families of nationalities with three granularity levels to use as our training data. Using a character-based machine learning model, we achieved a weighted F1 score of 84% for the broadest- and 67%, for the most granular, country-level categorization. In our empirical study, we used the trained and tested model to assign nationality to 8+ million scholars' full names in Scopus data. Our results show that using the country of first publication as a proxy for nationality underestimates the size of return flows, especially for countries with a more diverse academic workforce, such as the USA, Australia, and Canada. We found that around 48% of emigration from the USA was return migration once we used the country of name origin in contrast to 33% based on academic origin. In the most recent period, 79% of scholars ... |
| Τύπος εγγράφου: | dataset |
| Γλώσσα: | unknown |
| Relation: | https://zenodo.org/records/15103505; oai:zenodo.org:15103505; https://doi.org/10.5281/zenodo.15103505 |
| DOI: | 10.5281/zenodo.15103505 |
| Διαθεσιμότητα: | https://doi.org/10.5281/zenodo.15103505 https://zenodo.org/records/15103505 |
| Rights: | Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode |
| Αριθμός Καταχώρησης: | edsbas.A8ACEADC |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.5281/zenodo.15103505 |
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