Doctoral Dissertation Research: Exploring Sources of Information and Knowledge in Three Rural Language Varieties

All languages have resources that allow their speakers to express how they have acquired the information they communicate, and how confident they are regarding the possibility and probability that the events they express have occurred or will occur. Yet these resources vary widely – languages like English use optional words like ‘apparently’ or ‘maybe,’ while other languages use complex combinations of obligatory affixes or other grammatical resources.

These strategies are significant for communication – for example, a language that forces a speaker to be explicit about his or her information sources might lead to very different outcomes than one that does not. Moreover, knowledge may be expressed differently when it comes from the speaker, who can be sure about what they are thinking versus another person, whose thoughts we cannot know in the same way – and languages vary in their ‘rules’ for representing these differences.

This project contributes to our understanding of the cross-linguistic possibilities in the expression of these domains, which in turn sheds light on the range of human perspectives on how knowledge is obtained and represented.

This project addresses these questions in a set of language varieties spoken in remote rural communities, where speakers’ perspectives on knowledge and information source are likely to be maximally distinct from those represented in the urban and semi-urban contexts familiar to speakers of well-studied languages. The project will create a corpus of naturalistic speech and elicited data aimed toward exploring information source and levels of knowledge, and how they interact with the expression of time and other grammatical domains.

The work will leverage questionnaires, interviews, and natural discourse recorded in audio and video, which will be transcribed, translated, and annotated. The project employs software such as ELAN and FieldWorks Languages Explorer (FLEx) for morphosyntactic analysis and ensures the availability of the linguistic data by archiving the corpus.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.