Diachronic Dialectology: New Methods and Case Studies in Medieval Norwegian (Publications of the Philological Society) - Softcover

Über die Autorin bzw. den Autor

After a BA in Linguistics at the University of Essex, Tamsin Blaxter did her MPhil in General Linguistics and Comparative Philology at the University of Oxford, with a dissertation that explored gendered language in the medieval Íslendingasögur. For her PhD at the University of Cambridge, she worked on language change and space in medieval Norway. Her work focuses on new methods of gathering and exploring large datasets in variationist linguistics, with a particular interest in space.

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This book offers a solution to the problem of historical dialectology: how can we know how past dialect distributions evolved from noisy and unreliable historical sources? It is extremely difficult to answer such questions with traditional qualitative methods, while quantitative methods were previously prohibitively labour-intensive. But modern computing resources and digitised sources make these questions tractable.

The book presents the use of kernel smoothing, a family of methods adapted from fields such as signal processing, as a way to identify the true spatial distribution of linguistic forms at particular points in time. The use of kernel smoothing in historical dialectology and new approaches to parameter setting are discussed in chapter 2. The main part of the book then presents a series of case studies from the history of Norwegian. Some of the major phonological and morphosyntactic shifts which transformed the language from Old Norwegian through Middle to early Modern Norwegian are investigated. For the first time, the kernel smoothing method allows us to see how these changes spread from place to place, and these findings are used to throw light on a number of more general research questions of interest to an audience beyond Scandinavianists. Finally, a step-by-step guide to kernel smoothing is offered, so that non-experts can apply the approach to their own data.