Abstract

Language diversity is under threat. While each language is subject to specific social, demographic and political pressures, there may also be common threatening processes. We use an analysis of 6,511 spoken languages with 51 predictor variables spanning aspects of population, documentation, legal recognition, education policy, socioeconomic indicators and environmental features to show that, counter to common perception, contact with other languages per se is not a driver of language loss. However, greater road density, which may encourage population movement, is associated with increased endangerment. Higher average years of schooling is also associated with greater endangerment, evidence that formal education can contribute to loss of language diversity. Without intervention, language loss could triple within 40 years, with at least one language lost per month. To avoid the loss of over 1,500 languages by the end of the century, urgent investment is needed in language documentation, bilingual education programmes and other community-based programmes.

Highlights

  • IntroductionHigher average years of schooling is associated with greater endangerment, evidence that formal education can contribute to loss of language diversity

  • Understanding global threats to language diversity requires that we develop a macroecology of language endangerment and loss[16]

  • We use an endangerment scale based on EGIDS, which incorporates a range of factors including domains of use and intergenerational transmission[22,23]

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Summary

Introduction

Higher average years of schooling is associated with greater endangerment, evidence that formal education can contribute to loss of language diversity. We provide a global analysis to model patterns of current and future language endangerment, and compare the predictive power of variables representing some of the potential drivers of language loss. A macroecological approach has many advantages: it allows evaluation of a large range of factors that influence language vitality; formal testing of general patterns above the signal of individual language trajectories; statistical comparison of the explanatory power of different models, accounting for covariation of cultural, socioeconomic and environmental factors; and a way of avoiding the confounding effects of spatial distribution and relationships between languages[17].

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