Flerspråkighet i ett globaliserat samhälle : uppfattningar om minoritetsspråket som framtidsresurs

Multilingualism in a globalized society: The minority language as a future resource In this article the author investigates how the globalization of society is used as a reference in the discussion of future opportunities among minority language speaking youths in Sweden. A spatial typology of four different types of societies are constructed, the national, the multicultural, the diasporic and the transnational society, all giving the expression of different levels of globalization. These are used as layers of reference put upon the empirical data, functioning as a raster on a screen. The result is a pattern of expressions in three societal dimensions, the economic, the social and the cultural dimension. The findings of the investigation show that the minority language as a future resource of opportunities is anchored in all four societal types and in all three dimensions. In the empirical data (the youths interviewed) the ability of anchoring (finding stories, opportunities etc.) is less frequent when it comes to the diasporic and the transnational as a foundation for opportunity and more frequent when it comes to the national and the multicultural.

Fitting spatial models in the R package: hglm

We present a new version of the hglm package for fittinghierarchical generalized linear models (HGLM) with spatially correlated random effects. A CAR family for conditional autoregressive random effects was implemented. Eigen decomposition of the matrix describing the spatial structure (e.g. the neighborhood matrix) was used to transform the CAR random effectsinto an independent, but heteroscedastic, gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR model.This gives a computationally efficient algorithm for moderately sized problems (e.g. n<5000).

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.