Rethinking the matrix language: Vietnamese–English code-switching in Canberra

Research motivation:Myers-Scotton’s Matrix Language Framework (MLF) has long been extremely influential, claiming ‘universality of support, no matter which languages are involved’. Support for this model, however, has largely come from language pairs that are typologically different in terms of their clausal word order, or else have vastly different inventories of inflectional morphology. My aim in this work is thus to test the extent to which the underlying principles of the MLF can be applied to Vietnamese–English, that is, two languages that are both subject–verb–object (SVO) and highly analytic.Approach:I apply the MLF’s main principles, i.e. the System Morpheme Principle and the Morpheme Order Principle, to Vietnamese–English codeswitching data to determine the extent to which the matrix language of each utterance could be straightforwardly established.Data and analysis:Data come from the Canberra Vietnamese English corpus (CanVEC), which contains 45 speakers from both first- and second-generation immigrants. I provide both quantitative and qualitative analyses of the MLF performance on this dataset.Findings/conclusions:Results show that MLF model fails to account for the majority of the CanVEC bilingual data, including both first- and second-generation speakers’ production. I further highlight, on empirical grounds, the equivocal nature of assuming speakers’ monolingual code as a basis of comparison, the ‘Composite ML’ notion, and the assumption of null elements in mixed speech.Originality:This is the first study that examines Vietnamese–English language contact with respect to the matrix language, using natural language production from the Vietnamese–English bilingual community in Canberra. This is also the first that empirically shows how various parts of the MLF can be both quantitatively and qualitatively problematic.Significance/implications:This work addresses a lacuna in the field, where work on minority languages and their speech communities is still much more limited, especially in comparison to English and other Indo-European languages. The data and analysis offered here serve as a modest contribution in this direction, allowing us to address existing biases and to reexamine ‘universal’ assumptions of various kinds.