Knowledge constructions in research communities: The example of agri-rural researchers in Denmark

Construction of scientific knowledge can be seen as a struggle over who should define the terms and conditions of legitimate fields of research. Sociologists of scientific knowledge (SSK) have pointed to the importance of analysing scientific knowledge in the same way as other types of knowledge. This idea guides the present paper on Danish research in agriculture and rural areas. Based on an ethnographic study of researchers involved in rural studies, we take stock of the agri-rural research community in Denmark and reflect upon the how and why ‘fashions’ in Danish rural studies differ from ‘fashions’ in rural studies in the UK. In the analysis, we show how a research community construct and reconstruct itself in relation to what is perceived as legitimate fields of research. Finally, the paper gives insight into the research world of those doing research outside the UK and adds to the discussion of ‘putting philosophies of geography into practice’ that is on-going in British geography.     

Applying Image Norms Across Super's Career Development Stages

D. E. Super's (1957) theory of career development has long been of interest to careers researchers (M. Savickas, 1994; S. C. Whiston & B. K. Brecheisen, 2002). Its insightful illustration of career stages has made it widely applied by careers practitioners. Image norms may influence the career decisions and developmental tasks inherent in each stage. An image norm is the belief that individuals must present or possess a certain image, consistent with occupational, organizational, or industry standards, in order to achieve career success. Understanding the effects of image norms across D. E. Super's (1957) career stages has important implications for individuals, organizations, and career counseling professionals.     

Sektion Frauen- und Geschlechterforschung

Ohne Zusammenfassung     

Determinants and Consequences of Social Trust*

This article provides an overview of social trust, examining its various aspects and components. Trust is best understood in a sociological sense by focusing on its important relational characteristic. Following this lead, the article discusses briefly how social trust relates to social capital and examines factors that shape the development of social trust, along with outcomes related to variations in trust, classifying them by analytical level (i.e., individual, community, group, organizational, and societal). The article concludes by assessing the strengths and weaknesses of existing research and by identifying some important questions that have not yet been adequately addressed.     

Household strategies in the face of resource scarcity in coastal Ghana: are they associated with development priorities?

In many developing regions, women and young girls spend several hours daily in the collection of natural resources. Still the link between these household resource strategies and stakeholder perceptions of development priorities remains unexplored. This project examines this association with survey data representative of the adult population from Ghana’s Coastal Region. Although natural resource scarcity and the sustainability of resource use represent key development challenges, there are others (e.g., energy, sanitation, employment, and educational opportunities). As such, even in the face of natural resource scarcity, individuals may place greater importance on other dimensions of development, especially if household resource strategies are perceived as relatively efficient. The analytical focus here is on water and the results suggest that gender roles shape household water collection strategies, while also shaping these strategies’ perceived opportunity costs. Specifically, Ghanian adults more often see drinking water provision as their primary development need when water sources are distant and/or when male household members collect water (particularly male heads). In the end, I argue that social science inquiry benefits by contextualizing social dynamics within environmental context, particularly within cultural settings in which human subsistence is intimately tied to the state of the natural environment.     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.