Perspectives on social citizenship in the EU – from status positivus to status socialis activus via two forms of transnational solidarity

Ever since the inauguration of EU citizenship, elements of social citizenship have been on the agenda of European integration. European level social benefits were proposed early on, and demands for collective labour rights have followed suit. This chapter uses the theoretical umbrella of transnational social citizenship in order to link transnational access to social benefits and collective labour rights. It promotes transnational rights as the best way to conceptualise EU social citizenship as an institution enabling the enjoyment of EU integration without being forced to forego social rights at other levels. Such a perspective sits well in a collection on EU citizenship and federalism, since it simultaneously challenges demands of renationalisation of social rights in the EU and pleas to reduce EU-level citizenship rights to a merely liberal dimension. Social citizenship as promoted here requires an interactive conceptualisation of regulatory and judicial powers at different levels of government as typical for federal systems.
In linking citizenship with human rights the chapter highlights different statuses of citizens. It argues that the rights constituted by social citizenship derive from a status positivus and a status socialis activus, expanding the time-honoured categories of Jellinek. This concept is developed further by linking the notions of receptive solidarity to the status positivus and the notion of participative solidarity to the status socialis activus. In relation to European Union citizenship it promotes a sustainable transnational social citizenship catering for receptive and participative solidarity.
These ideas contrast with most current discourses on EU citizenship. The stress on social citizenship takes issue with a retreat to mere liberalist notions of EU-level citizenship, and the stress on rights takes issue with conceptualising EU citizenship as a community bond with obligations, downplaying the empowering potential of rights. The difficulty of conceptualising transnational social citizenship is to avoid, on the one hand, taking up the tune of populist discourses imagining those moving beyond state borders as a threat to national social citizenship and, on the other hand, to reject the legitimate fears of those remaining at home of creating rupture in the social fabric of Europe’s society. Promoting transnational social citizenship rights based on receptive and participative solidarity the present chapter aims to contribute to avoiding these pitfalls.

Functional bilingualism: Definition and ways of assessment

The purpose of the article is to research an issue concerning the bilingual status of students majoring in English as a foreign language at Tomsk State University and similar universities. The authors focus on a brief description of types of bilingualism to define the category best suited to the EFL students and discuss the complementing uses of traditionally accepted (TOEFL+ etc.) and alternative language assessment procedures (The Peabody Picture Vocabulary Test) for the stated target group to assess their competence in international education and balanced cross-cultural communication.

Generating a Word-Emotion Lexicon from #Emotional Tweets

Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classi- fication performance.