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Travel time as a predictor of linguistic distance

Authors: Gooskens, C. S.;

Travel time as a predictor of linguistic distance

Abstract

The aim of the present investigation 1 was to get an impression of the geographic influences on the dialectal variation in a country. In previous investigations, the correlations between linguistic distances and geographic distances using dialect data from the Netherlands and Norway were calculated (Gooskens and Heeringa 2004, Nerbonne et al. 1996). The results showed a high correlation in the case of Dutch data while the correlation was considerably lower in the case of Norwegian data. This seems to reflect the fact that especially for Norway the direct distance between two settlements does not reflect the difficulty of travel and therefore social contact, which is expected to play a role in keeping linguistic distance within limits. Holland is a country with a flat, regularly populated landscape with few natural obstacles such as mountains and rivers. This is in great contrast with Norway with its high mountains and many fjords which made it quite difficult to travel between places, especially in the past. These differences in geographical situations are clearly reflected in the correlations between the linguistic and geographical distances between the dialects of the two countries. The present investigation is searching for more successful ways of predicting linguistic distances by means of geographic distances in Norway. To this end, old and new traveling data were used providing information about traveling times by road, train, and boat between the places where the different dialects are spoken. The results show that a large part of the linguistic variation can be accounted for by geography in Norway, just as in the Netherlands. However, in the case of a geographically more compli

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Netherlands
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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!
26
Top 10%
Top 10%
Average
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