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OALib Journal期刊
ISSN: 2333-9721
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-  2018 

Large

DOI: 10.1177/0759106318778828

Keywords: Onomastics,machine learning,diversity,representativeness,geographical origins,Onomastique,apprentissage machine,diversité,représentativité,origines géographiques

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Abstract:

The study of surnames as both linguistic and geographical markers of the past has proven valuable in several research fields spanning from biology and genetics to demography and social mobility. This article builds on the existing literature to conceive and develop a surname origin classifier based on a data-driven typology. This enables us to explore a methodology to describe large-scale estimates of the relative diversity of social groups, especially when such data is scarcely available. We subsequently analyze the representativeness of surname origins for 15 socio-professional groups in France

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