By Lars Borin
The current quantity collects contributions addressing diverse elements of the size of linguistic transformations, a subject matter which most likely is as previous as language itself yet even as has bought renewed curiosity during the last decade or so, reflecting a speedy improvement of data-intensive computing in all fields of analysis, together with linguistics.
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Additional info for Approaches to Measuring Linguistic Differences
A further problem (which counts for Arawak as well as other Amazonian languages with no old written sources) is constituted by the great difficulties in defining relative chronologies, a prerequisite for distinguishing true cognates from early loans in cultural vocabulary. However, the layering technique, enabled by the GIS-technology, opens a number of possibilities. Here, individual items of interest, be it crops, kinships terms, flutes or names for various animals, could be either lumped with other items of the basic vocabulary and used in clusters, or projected (against maps) either individually or in groups of relevance.
1. Lexical data The lexical data in the project are selected and applied in accordance with two basic parameters: (1) expected stability versus contact sensitivity, (2) correlation with the features distinguished in the matrix (see section 1). Both these parameters might be problematic, in particular if seen as oppositional. At a general level, studies of typology of borrowability like Haspelmath and Tadmor (2009) are indeed useful, but at a regional level, a number of separate factors have to be taken into consideration before stability or contact sensitivity is calculated.
It is interesting to see that these two clusters share close lexical similarities despite no indications of socio-cultural contacts between speakers of these languages for at least a millennium (Eriksen 2011). Regarding the spatial distribution of clusters A, C, and D, their positions are located at reasonably expected distances from each other given some kind of geographical diffusion process from a common proto-language. This is also the case for clusters E and F, and in the case of Apurinã, Machinere, Iñapari, and Piro of cluster E, it indicates that these languages rather share a common history with Marawá and Waraikú, the two other languages of cluster E, and ultimately with the Arawak languages of the northwest Amazon, than with the languages of cluster I; Asháninka, 48â•‡â•‡ Gerd Carling, Love Eriksen, Arthur Holmer and Joost van de Weijer Caquinte, Machiguenga, Nomatsiguenga.
Approaches to Measuring Linguistic Differences by Lars Borin