Arbetsorganisation i träbränsleeldande fjärrvärmeverk

The objective of this study has been to describe and analyse existing forms of organisation in heating plants using wood fuels, regarding work tasks, organisational structure, skill demands, crew recruitment, working hours and wage conditions. Sixteen plants ranging from 10 to 120 MW have been studied by means of interviews, work place observations and written material. The job of the operator of heating plants is fairly qualified, independent and varied. The most negative factor is shift work. Some of the bigger plants (enterprises) have a relatively hierarchic, segmented and perhaps also an oversized organisation. However, modern concepts of organisation, such as customer orientation, ”flat organisation”, integration of production and maintenance etc, are gaining ground. Blue collar and white collar tasks are increasingly being integrated. Some of the medium sized enterprises have reached very far and may serve as models for bigger enterprises.

Vem är den ekonomiske brottslingen? : En jämförelse mellan länder och brottstyper

Who is the economic criminal? A comparison between countries and types of crime In white collar crime research two particularly competing definitions (Sutherland versus the Revisionists) have dominated the field during the last two decades. Sutherland’s definition states that the sociodemographic profile is homogeneous (entrepreneur with high education and high or regular income), despite type of white collar crime or context. The definition given by the Revisionists states that white collar criminals’ demographic profile is heterogeneous (everyone can be convicted for white collar crime). As a consequence of this divided definitional approach we have a contradictive outcome of who the white collar criminal is. Our purpose is to investigate the qualification of the two definitions by analyzing heterogeneity/ homogeneity based on crime type and national context. The investigation is based on seven countries from the EES 2004 (European Social Survey). We use four types of crime. The results show a rather homogeneous demographic profile but there is also a certain substantial heterogeneity depending on kinds of crime and context. The results altogether indicate that the Revisionists’ definition is more correct in its description of the white collar criminal than Sutherland’s definition. The demographic profile of the white collar criminal seems to be more complex than a profile confined to just one social category would be and the contextual factor has an impact on the variety of the demographic profile. An important task for future research is to hold the door open for further demographic investigations depending on the type of crime and country that the study is based on. 

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.