Universität und solidarische Ökonomie

RESUMO: A economia solidária é aqui apresentada como um movimento social emancipatório e como uma das formas de resistências das trabalhadoras e trabalhadores ao modelo de desenvolvimento capitalista. O movimento contemporâneo de economia solidária abrange o processo de produção, comercialização e finanças. A economia solidária é caracterizada pela posse coletiva dos meios de produção e pelo controle dos trabalhadores dos empreendimentos através de autogestão, cooperação e solidariedade. Os empreendimentos econômicos solidários se organizam sob a forma de cooperativas, associações e grupos informais. Um dos maiores desafios da economia solidária está no campo educativo, porque impõe a desconstrução dos princípios individualistas e privatistas preponderantes na maioria das relações econômicas, e exige a construção de outra cultura pautada na solidariedade. Nesse sentido, a pesquisa realizada, tem como objeto de estudo as metodologias de incubação fomentadas pelas universidades nas ações de economia solidária. Para isso, analisamos as experiências da Incubadora de Economia Solidária da Universidade Federal da Paraíba - Brasil e da Incubadora na Universidade de Kassel- Alemanha – Verein für Solidarische Ökonomie e.V. A pesquisa buscou conhecer e analisar as práticas de incubagem das universidades na economia solidária, como processos de mudança social. A coleta de informações foi realizada, tendo por base, uma revisão bibliográfica, relatórios das Incubadoras, registros fotográficos, observação participante e entrevistas semi-estruturadas. Os resultados da análise indicam que as metodologias de incubação na economia solidaria, por terem um caráter aberto e participativo, por considerarem os condicionamentos históricos e as diferentes culturas, fazem-nas portadoras de mudanças sociais. Esta metodologia pode ser utilizada por diferentes atores, em lugares e situações distintas. A pesquisa indica ainda, a centralidade da questão ecológica como elemento que poderá unificar o movimento internacional de economia solidária.

Re-Framing Trade

Given the substantial and increasing encroachment of trade agreements into almost every aspect of economic and social life, there is a pressing need for research that provides a more coherent framework for understanding the source and effectiveness of organised labour ’s power and capacity to influence international trade policy. Taking the union protests against the General Agreement on Trade in Services (GATS) as a case study, this research uses core concepts derived from social movement theory to analyse the opportunities that existed for unions to influence these trade negotiations and their capacity to identify and take advantage of such opportunities. Importantly, it adds a power analysis designed to reveal the sources of power that unions draw on to take action. The research demonstrates that even where unions faced considerable constraints they were able to re-frame trade issues in a way that built broad support for their position and to utilise opportunities in the trade negotiation process to mobilise resistance against the GATS and further liberalisation of services. The theoretical framework developed for the research provides conceptual tools that can be developed for improving strategic campaign planning and for analytical assessment of past campaigns. The theoretical framework developed for this research has potential for further application as an analytical and strategic planning tool for unions.

Capturing Emergent Semantics from Social Annotation Systems

The ongoing growth of the World Wide Web, catalyzed by the increasing possibility of ubiquitous access via a variety of devices, continues to strengthen its role as our prevalent information and commmunication medium. However, although tools like search engines facilitate retrieval, the task of finally making sense of Web content is still often left to human interpretation. The vision of supporting both humans and machines in such knowledge-based activities led to the development of different systems which allow to structure Web resources by metadata annotations. Interestingly, two major approaches which gained a considerable amount of attention are addressing the problem from nearly opposite directions: On the one hand, the idea of the Semantic Web suggests to formalize the knowledge within a particular domain by means of the "top-down" approach of defining ontologies. On the other hand, Social Annotation Systems as part of the so-called Web 2.0 movement implement a "bottom-up" style of categorization using arbitrary keywords. Experience as well as research in the characteristics of both systems has shown that their strengths and weaknesses seem to be inverse: While Social Annotation suffers from problems like, e. g., ambiguity or lack or precision, ontologies were especially designed to eliminate those. On the contrary, the latter suffer from a knowledge acquisition bottleneck, which is successfully overcome by the large user populations of Social Annotation Systems. Instead of being regarded as competing paradigms, the obvious potential synergies from a combination of both motivated approaches to "bridge the gap" between them. These were fostered by the evidence of emergent semantics, i. e., the self-organized evolution of implicit conceptual structures, within Social Annotation data. While several techniques to exploit the emergent patterns were proposed, a systematic analysis - especially regarding paradigms from the field of ontology learning - is still largely missing. This also includes a deeper understanding of the circumstances which affect the evolution processes. This work aims to address this gap by providing an in-depth study of methods and influencing factors to capture emergent semantics from Social Annotation Systems. We focus hereby on the acquisition of lexical semantics from the underlying networks of keywords, users and resources. Structured along different ontology learning tasks, we use a methodology of semantic grounding to characterize and evaluate the semantic relations captured by different methods. In all cases, our studies are based on datasets from several Social Annotation Systems. Specifically, we first analyze semantic relatedness among keywords, and identify measures which detect different notions of relatedness. These constitute the input of concept learning algorithms, which focus then on the discovery of synonymous and ambiguous keywords. Hereby, we assess the usefulness of various clustering techniques. As a prerequisite to induce hierarchical relationships, our next step is to study measures which quantify the level of generality of a particular keyword. We find that comparatively simple measures can approximate the generality information encoded in reference taxonomies. These insights are used to inform the final task, namely the creation of concept hierarchies. For this purpose, generality-based algorithms exhibit advantages compared to clustering approaches. In order to complement the identification of suitable methods to capture semantic structures, we analyze as a next step several factors which influence their emergence. Empirical evidence is provided that the amount of available data plays a crucial role for determining keyword meanings. From a different perspective, we examine pragmatic aspects by considering different annotation patterns among users. Based on a broad distinction between "categorizers" and "describers", we find that the latter produce more accurate results. This suggests a causal link between pragmatic and semantic aspects of keyword annotation. As a special kind of usage pattern, we then have a look at system abuse and spam. While observing a mixed picture, we suggest that an individual decision should be taken instead of disregarding spammers as a matter of principle. Finally, we discuss a set of applications which operationalize the results of our studies for enhancing both Social Annotation and semantic systems. These comprise on the one hand tools which foster the emergence of semantics, and on the one hand applications which exploit the socially induced relations to improve, e. g., searching, browsing, or user profiling facilities. In summary, the contributions of this work highlight viable methods and crucial aspects for designing enhanced knowledge-based services of a Social Semantic Web.