Lifelong education and transnational migration

ABSTRACT

The purpose of this paper is to investigate whether and through what mechanisms vulnerable adults are empowered through a second-chance education programme. At the same time, the paper aims at unveiling the obstacles hindering learners’ empowerment process and making suggestions for the improvement of the educational programme fostering further empowerment of the learners. To achieve the objectives set by the research, a hybrid methodological design was applied combining hermeneutic phenomenology and critical discourse analysis. Data collection was performed using three tools: semi-structured interviews, reflective journals and document analysis. The results led to the emergence of a multilevel empowerment scheme of vulnerable adults in the programme. The empowerment mechanisms that emerged were as follows: (a) empowerment through participation as a self-value, (b) empowerment through the reconstruction of past experiences, (c) empowerment through the strengthening of their social capital and (d) empowerment through literacy skills. At the same time, the study shed light on a number of factors that inhibit the process of learners’ empowerment, which unveil structural and operational weaknesses of the programme as well as of the policies pursued by the State.     

COUNTER 2005: a new Code of Practice and new applications of COUNTER usage statistics

The article gives an update on the progress with COUNTER to May 2005. In particular it describes the draft Code of Practice for books and reference works and the results of actual use of COUNTER‐compliant data in the journals area.     

Efficient reverse k-nearest neighbor estimation

The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. Many industrial and scientific applications call for solutions of the RkNN problem in arbitrary metric spaces where the data objects are not Euclidean and only a metric distance function is given for specifying object similarity. Usually, these applications need a solution for the generalized problem where the value of k is not known in advance and may change from query to query. In addition, many applications require a fast approximate answer of RkNN-queries. For these scenarios, it is important to generate a fast answer with high recall. In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our approach uses the advantages of existing metric index structures but proposes to use an approximation of the nearest-neighbor-distances in order to prune the search space. We show that our method scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.     

Government information policy principles

This article analyzes the utility of policy principles advanced by professional associations.