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Article

Social Network Approach in African Sociolinguistics  

Klaus Beyer and Henning Schreiber

The Social Network Analysis approach (SNA), also known as sociometrics or actor-network analysis, investigates social structure on the basis of empirically recorded social ties between actors. It thereby aims to explain e.g. the processes of flow of information, spreading of innovations, or even pathogens throughout the network by actor roles and their relative positions in the network based on quantitative and qualitative analyses. While the approach has a strong mathematical and statistical component, the identification of pertinent social ties also requires a strong ethnographic background. With regard to social categorization, SNA is well suited as a bootstrapping technique for highly dynamic communities and under-documented contexts. Currently, SNA is widely applied in various academic fields. For sociolinguists, it offers a framework for explaining the patterning of linguistic variation and mechanisms of language change in a given speech community. The social tie perspective developed around 1940, in the field of sociology and social anthropology based on the ideas of Simmel, and was applied later in fields such as innovation theory. In sociolinguistics, it is strongly connected to the seminal work of Lesley and James Milroy and their Belfast studies (1978, 1985). These authors demonstrate that synchronic speaker variation is not only governed by broad societal categories but is also a function of communicative interaction between speakers. They argue that the high level of resistance against linguistic change in the studied community is a result of strong and multiplex ties between the actors. Their approach has been followed by various authors, including Gal, Lippi-Green, and Labov, and discussed for a variety of settings; most of them, however, are located in the Western world. The methodological advantages could make SNA the preferred framework for variation studies in Africa due to the prevailing dynamic multilingual conditions, often on the backdrop of less standardized languages. However, rather few studies using SNA as a framework have yet been conducted. This is possibly due to the quite demanding methodological requirements, the overall effort, and the often highly complex linguistic backgrounds. A further potential obstacle is the pace of theoretical development in SNA. Since its introduction to sociolinguistics, various new measures and statistical techniques have been developed by the fast growing SNA community. Receiving this vast amount of recent literature and testing new concepts is likewise a challenge for the application of SNA in sociolinguistics. Nevertheless, the overall methodological effort of SNA has been much reduced by the advancements in recording technology, data processing, and the introduction of SNA software (UCINET) and packages for network statistics in R (‘sna’). In the field of African sociolinguistics, a more recent version of SNA has been implemented in a study on contact-induced variation and change in Pana and Samo, two speech communities in the Northwest of Burkina Faso. Moreover, further enhanced applications are on the way for Senegal and Cameroon, and even more applications in the field of African languages are to be expected.

Article

Genealogical Classification in Historical Linguistics  

Søren Wichmann

Different methods exist for classifying languages, depending on whether the task is to work out the relations among languages already known to be related—internal language classification—or whether the task is to establish that certain languages are related—external language classification. The comparative method in historical linguistics, developed during the latter part of the 19th century, represents one method for internal language classification; lexicostatistics, developed during the 1950s, represents another. Elements of lexicostatistics have been transformed and carried over into modern computational linguistic phylogenetics, and currently efforts are also being made to automate the comparative method. Recent years have seen rapid progress in the development of methods, tools, and resources for language classification. For instance, computational phylogenetic algorithms and software have made it possible to handle the classification of many languages using explicit models of language change, and data have been gathered for two thirds of the world’s language, allowing for rapid, exploratory classifications. There are also many open questions and venues for future research, for instance: What are the real-world counterparts to the nodes in a family tree structure? How can shortcomings in the traditional method of comparative historical linguistics be overcome? How can the understanding of the results that computational linguistic phylogenetics have to offer be improved? External language classification, a notoriously difficult task, has also benefitted from the advent of computational power. While, in the past, the simultaneous comparison of many languages for the purpose of discovering deep genealogical links was carried out in a haphazard fashion, leaving too much room for the effect of chance similarities to kick in, this sort of activity can now be done in a systematic, objective way on an unprecedented scale. The ways of producing final, convincing evidence for a deep genealogical relation, however, have not changed much. There is some room for improvement in this area, but even more room for improvement in the way that proposals for long-distance relations are evaluated.