ArticleProceedings of the National Academy of Sciences of the United States of America2017
Quantifying the driving factors for language shift in a bilingual region.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Minority identity and social structures shape diffusion dynamics of minority languages: a combined macro and micro approach.Royal Society open science · 2025Article
- Measures to sustain endangered languages: A bilingual competition model with sliding mode control.PloS one · 2023Article
- Cumulative cultural evolution and mechanisms for cultural selection in wild bird songs.Nature communications · 2022Article
- Language and ethnobiological skills decline precipitously in Papua New Guinea, the world's most linguistically diverse nation.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
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- Kia kaua te reo e rite ki te moa, ka ngaro: do not let the language suffer the same fate as the moa.Journal of the Royal Society, Interface · 2020Article
- Modeling language shift.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many of the world's around 6,000 languages are in danger of disappearing as people give up use of a minority language in favor of the majority language in a process called language shift. Language shift can be monitored on a large scale through the use of mathematical models by way of differential equations, for example, reaction-diffusion equations. Here, we use a different approach: we propose a model for language dynamics based on the principles of cellular automata/agent-based modeling and combine it with very detailed empirical data. Our model makes it possible to follow language dynamics over space and time, whereas existing models based on differential equations average over space and consequently provide no information on local changes in language use. Additionally, cellular automata models can be used even in cases where models based on differential equations are not applicable, for example, in situations where one language has become dispersed and retreated to language islands. Using data from a bilingual region in Austria, we show that the most important factor in determining the spread and retreat of a language is the interaction with speakers of the same language. External factors like bilingual schools or parish language have only a minor influence.
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