Heritage language loss and the implications for ethnic identity maintenance over time

Language has been seen as a central pillar to ethnic identity. When the possibility of heritage language loss becomes imminent, therefore, concern turns towards the consequences for feelings of ethnic group membership. Heritage language researchers have indicated that the heritage language is so strongly associated with the individual’s cultural background that heritage language loss could have negative implications for the sense of identity to the ethnic group. This study investigates the relationship between language and ethnic identity over time among Gaelic learners in Nova Scotia. In order to identify the specific processes of heritage language use, the Gaelic learners are compared to French (second language), learners living in the same English-speaking milieu. Path analyses reveal that, only among Gaelic learners, there is an initial separation of language and ethnic identity, but that, over time, ethnic identity is a direct outcome of language use. The results support Edwards’ (1985), contention, at least in the case of heritage languages, that language and identity are not always strongly linked. It is suggested that this may be especially true in contexts where there is little opportunity for contact with members of the heritage language group.

Language Planning and Policy: Recent Trends, Future Directions

A framework for and overview of the key elements of language planning is presented covering status planning, corpus planning, language-in-education planning, prestige planning and critical approaches to language planning. Within each of these areas, key articles outlining important recent directions are discussed indicating the field’s new found sense of vitality.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.