scholarly journals Emergent sociolinguistic variation in severe language endangerment

2019 ◽  
Vol 48 (5) ◽  
pp. 685-720 ◽  
Author(s):  
Jonathan R. Kasstan
Keyword(s):  
Language Variation ◽  
Sociolinguistic Variation ◽  
Language Variation And Change ◽  
Language Death ◽  
Language Endangerment ◽  
Form Part ◽  
New Speakers ◽  
Style Shifting ◽  
Fluent Speakers

ABSTRACTContrary to Labov's principle of style shifting, studies in language obsolescence portray speakers of dying languages as ‘monostylistic’, a characterization questioned here. Variationist methodology is adopted in a context of gradual language death. By combining quantitative and interactional analyses of data from older, younger, and new speakers of Francoprovençal in France and Switzerland, the article considers (a) to what extent variability in language obsolescence differs from that found in ‘healthy’ languages, and (b) how innovations might spread through communities speaking threatened languages characterized as ‘monostylistic’ and lacking overt normative infrastructure. It is argued that style variation (not monostylism) emerges from linguistic decay: among more fluent speakers, a categorical rule of /l/-palatalization before obstruents becomes underspecified, rendering palatalization available for strategic use. Among new speakers, novel palatal variants form part of an emergent sociolinguistic norm. The study offers fresh insights on the origins of sociolinguistic variation with implications for variationist theory. (Language obsolescence, language death, language variation and change, style variation, new speakers)*

scholarly journals Registerial Adaptation vs. Innovation Across Situational Contexts: 18th Century Women in Transition

2021 ◽  
Vol 4 ◽  
Author(s):  
Stefania Degaetano-Ortlieb ◽  
Tanja Säily ◽  
Yuri Bizzoni
Keyword(s):  
Language Use ◽  
18Th Century ◽  
Temporal Dynamics ◽  
Language Variation ◽  
Sociolinguistic Variation ◽  
Language Variation And Change ◽  
Situational Contexts ◽  
Leibler Divergence ◽  
Long Term Trends

Endeavors to computationally model language variation and change are ever increasing. While analyses of recent diachronic trends are frequently conducted, long-term trends accounting for sociolinguistic variation are less well-studied. Our work sheds light on the temporal dynamics of language use of British 18th century women as a group in transition across two situational contexts. Our findings reveal that in formal contexts women adapt to register conventions, while in informal contexts they act as innovators of change in language use influencing others. While adopted from other disciplines, our methods inform (historical) sociolinguistic work in novel ways. These methods include diachronic periodization by Kullback-Leibler divergence to determine periods of change and relevant features of variation, and event cascades as influencer models.

Dialectology

2018 ◽  
Author(s):  
Becky Childs
Keyword(s):  
Systematic Study ◽  
Online Survey ◽  
Historical Linguistics ◽  
Social Space ◽  
Survey Methods ◽  
Social Groups ◽  
Language Variation ◽  
Technological Innovations ◽  
Language Variation And Change ◽  
Language Endangerment

The field of dialectology, the study of the language of an area or group of people, has a long tradition within linguistics. From the earliest dialect studies, a focus on rigorous methodological practices has been an ever-present component of this discipline. Traditional methodologies can be seen in the work of the early dialect atlases, which relied heavily on mail questionnaires or fieldworkers that would chronicle the pronunciation, grammatical features, and lexicon of residents of particular regions. More recent technological innovations, such as GIS and online survey methods and applications, have brought multidisciplinary approaches to the study of dialects, as well as allowing for broader and more robust studies of geographic areas and social groups. The influence and interface of dialectology on various linguistic disciplines is noteworthy. Dialectological methods have most commonly been utilized in historical linguistics, sociolinguistics/language variation and change, and language endangerment/documentation. Within each of these disciplines, the adoption of methods from dialectology has allowed for the systematic study of language across geographic and social space, as well as across time.

Hands off the metadata!: Comparing the use of explicit and background metadata in crowdsourced dialectology

2021 ◽  
Vol 7 (s1) ◽  
Author(s):  
Tamsin Blaxter ◽  
David Britain
Keyword(s):  
Language Variation ◽  
Social Background ◽  
Smartphone Application ◽  
Background Information ◽  
British Isles ◽  
Language Variation And Change ◽  
Digital Era ◽  
The Social ◽  
Dialect Variation ◽  
Regional Dialect

Abstract In this article we assess the extent to which we can collect plausible data about regional dialect variation using crowdsourcing techniques – the BBC Future Survey – without explicitly gathering any user metadata, but relying instead on background information collected by Google Analytics. In order to do this, we compare this approach with another crowdsourced survey, operated from a smartphone application, which examines the same site – the British Isles – but which explicitly asks users to submit detailed social background information – the English Dialects App (EDA) (Leemann et al. 2018). The EDA has the disadvantage that there is a considerable user drop-off between completing the dialect survey and completing the social metadata questionnaire. The BBC Future Survey, however, only collects information on where users are physically located when they complete the survey – not where they are from or even where they live. Results show that the BBC Future Survey produces a plausible snapshot of regional dialect variability that can complement other more sophisticated (expensive, time-consuming) approaches to investigating language variation and change. We suggest the approach constitutes a digital-era rapid anonymous survey along the lines of Labov (1972), serving similar aims, with similar success, but on a much much larger scale.

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