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Quantifying the relationship between digitization and linguistic diversity

Authors: Zeh, Katharina;

Quantifying the relationship between digitization and linguistic diversity

Abstract

Die weltweite Sprachdiversität ist in den vergangenen Jahrzehnten deutlich zurückgegangen. Es ist zu erwarten, dass sich dieser Trend fortsetzt, sofern keine wirksamen Gegenmaßnahmen ergriffen werden. Deshalb ist es umso wichtiger die zugrundeliegenden Faktoren hinter dieser Entwicklung zu identifizieren und zu analysieren. Einen Beitrag dazu leistet diese Arbeit, indem sie den bisher kaum erforschten Einfluss der Digitalisierung auf die globale Sprachdiversität und Sprachgefährdung untersucht. Die Analyse erfolgt mittels eines statistischen Mehrebenenansatzes, der in der Programmiersprache R durchgeführt wird. Auf Grundlage von Digitalisierungsindizes wie dem Mobile Connectivity Index, dem Digital Adoption Index und dem E-Government Development Index werden mithilfe von Clusteranalysen übergeordnete Muster und Gruppierungen identifiziert. Ergänzend wird der konzeptuelle Umfang dieser Indizes durch eine korrelationsbasierte Analyse untersucht. Anschließend wird der Zusammenhang zwischen Digitalisierung und Sprachdiversität anhand verschiedener Sprachdiversitätsmaße – der Anzahl der in einem Land gesprochenen Sprachen, eines auf Entropie basierenden Maßes und eines adaptierten Red List Index (RLI) – mittels einer Korrelationsanalyse analysiert. Die Ergebnisse der Arbeit deuten darauf hin, dass Digitalisierung nur geringe Auswirkungen auf Sprachgefährdung und sprachliche Vielfalt im engeren Sinne hat, jedoch einen signifikanten Einfluss auf die Verteilung von Sprachen innerhalb eines Landes ausübt. Diese Erkenntnisse bieten eine wertvolle Grundlage für zukünftige Forschung und können Entscheidungsträger:innen sowie Sprachaktivist:innen bei der Erhaltung und Förderung von Sprachdiversität unterstützen.

Linguistic diversity has declined significantly worldwide over the past few decades. This trend is expected to continue unless effective countermeasures are implemented, making it crucial to identify and analyse its underlying drivers. To contribute to this effort, this thesis examines the impact of digitization on global language diversity and endangerment—a relationship that has remained relatively underexplored in previous research. The investigation is carried out using a statistical multi-level approach implemented in the r programming language. Drawing on digitization indices such as the Mobile Connectivity Index, the Digital Adoption Index, and the E-Government Development Index, broader patterns and groupings are identified through cluster analysis, complemented by a correlation-based evaluation of the conceptual breadth of these indices. Subsequently, relationships between digitization and linguistic diversity—specifically, the number of languages spoken in each country, entropy-based measures, and an adapted Red List Index (RLI)—are explored through correlation analysis. The results suggest that while digitization has minimal effects on language endangerment and richness, it significantly influences the distribution of languages at the national level. These findings offer a valuable foundation for future research and may inform policymakers and language activists in efforts to preserve and promote linguistic diversity.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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