Challenges for a shared European countryside of uncertain future. Towards a modern community based landscape perspective

This paper addresses current changes in the highly diverse European landscape, and the way these transitions are being treated in policy and landscape management in the fragmented, heterogeneous and dynamic context of today’s Europe. It appears that intersecting driving forces are increasing the complexity of European landscapes and causing polarising developments in agricultural land use, biodiversity conservation and cultural landscape management. On the one hand, multifunctional rural landscapes, especially in peri-urban regions, provide services and functions that serve the citizens in their demand for identity, support their sense of belonging and offer opportunities for recreation and involvement in practical landscape management. On the other hand, industrial agricultural production on increasingly large farms produces food, feed, fibre and energy to serve expanding international markets with rural live ability and accessibility as a minor issue. The intermediate areas of traditionally dominant small and family farms in Europe seem to be gradually declining in profitability. The paper discusses the potential of a governance approach that can cope with the requirement of optimising land-sharing conditions and community-based landscape development, while adapting to global market conditions.

Experiments on identification of argumentative sentences

The main purpose of this study is to evaluate the best set of features that automatically enables the identification of argumentative sentences from unstructured text. As corpus, we use case laws from the European Court of Human Rights (ECHR). Three kinds of experiments are conducted: Basic Experiments, Multi Feature Experiments and Tree Kernel Experiments. These experiments are basically categorized according to the type of features available in the corpus. The features are extracted from the corpus and Support Vector Machine (SVM) and Random Forest are the used as Machine learning algorithms. We achieved F1 score of 0.705 for identifying the argumentative sentences which is quite promising result and can be used as the basis for a general argument-mining framework.