Barbara Sonnenhauser

Migration events splitting speaker communities and establishing novel contact situations are among the major drivers of language variation and change. While the precise processes that lead to change cannot usually be determined for past events with any certainty, the study of minority and heritage language usage in apparent time may provide insight into the contribution of the linguistic behavior underlying the dynamics. We capitalize on this and compare parts of speech usage in Pear Story renarrations across Gheg Albanian speakers of three generations in German-speaking environments, applying methods from information theory. The results suggest that the changing conventions in parts of speech usage across generations and places of residence can be attributed to changing linguistic behavior within the speaker community in the migration setting. These findings highlight the impact of changing sociocultural embedding and the roles of vertical and horizontal transmission in language change.

A corpus-based method for assessing a range of dialect-standard variation is presented for identifying samples exhibiting the highest prevalence of dialect features. This method provides insight into areal and inter-speaker variation and allows the extraction of maximally non-standard manifestations of the dialect, which may then be sampled and used for the study of language change and variation. The focus is on a non-standard Torlak variety, which has undergone considerable change under the influence of standard Serbian. The degree of variation is assessed by measuring the frequencies of five distinguishing linguistic features: accent position, dative reflexive si , auxiliary omission in the compound perfect, the post-positive article, and analytic case marking in the indirect object and possessive. Locations subject to the greatest and least influence of the standard are revealed using hierarchical clustering. A positive correlation between the frequencies of occurrence reveals which non-standard feature is the best predictor of the others.