Language choice of the Foochows in Sarawak, Malaysia

This paper investigates the factors affecting the language choices of the Chinese Foochows of Sarawak, focusing in particular on how the use of the Foochow dialect vis-a`-vis English and other languages might potentially result in a shift in language allegiance away from Foochow. In the context of Sarawak, the Foochows are a substantial, cohesive and homogeneous Chinese ethnic group with a distinctive language and ethnic identity. One would predict that they would engage in extensive language maintenance behaviour. Instead, Foochows living in non-Foochow dominant areas do not seem to have sufficient attachment to the language to transmit it to the next generation. Is this because the Foochows consider that accommodating to communicative norms is more important than preserving their native language as an inherent symbol of their ethnic identity? Or is it the result of the Foochows’ insecurity about the prestige of the dialect and the status of the Foochow people? These issues of accommodation and language allegiance are discussed, based on interview and questionnaire data from 11 Foochow participants. This data set is part of a larger study on the language use of different ethnic groups in multilingual organisational settings in Sarawak.

Three poverties

This paper is a discourse on ideological communication on poverty in Latin America from three different perspectives: the Catholic priest, the Shining Path guerrilla and the development economist. The paper concludes with a discussion of these three perspectives from the perspective of the theory of communicative action espoused by Jurgen Habermas.

Learning the dynamics of embedded clauses

Recent work by Siegelmann has shown that the computational power of recurrent neural networks matches that of Turing Machines. One important implication is that complex language classes (infinite languages with embedded clauses) can be represented in neural networks. Proofs are based on a fractal encoding of states to simulate the memory and operations of stacks. In the present work, it is shown that similar stack-like dynamics can be learned in recurrent neural networks from simple sequence prediction tasks. Two main types of network solutions are found and described qualitatively as dynamical systems: damped oscillation and entangled spiraling around fixed points. The potential and limitations of each solution type are established in terms of generalization on two different context-free languages. Both solution types constitute novel stack implementations - generally in line with Siegelmann's theoretical work - which supply insights into how embedded structures of languages can be handled in analog hardware.