49,505 research outputs found

    PROFILOWANIE PRAWNO-JĘZYKOWE W OSADZENIU INSTYTUCJONALNYM – NA PRZYKŁADZIE PRACOWNICZYCH ORGANÓW PRZEDSTAWICIELSKICH W UE

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    This paper applies a structured legal-linguistic profiling approach to EU “staff representation bodies” as a way to access domains that lie behind the public face of EU institutions and their texts concerning translation, language and terminology. The study commences with a legal-linguistic analysis of EU texts for references to “staff”, “staff representation” and “employment” in order to identify specific texts and bodies of relevance to the study. This approach leads to two broad categories: staff committees and trade unions. Information is sought from EU institutions about these bodies and their translation and language arrangements, and a list is made of websites available to the general public. These sites are then examined as part of the legal-linguistic profiling approach.W niniejszym artykule zastosowano ustrukturyzowane podejście do profilowania prawno-językowego do „unijnych organów reprezentujących pracowników” jako sposobu dostępu do obszarów poza oficjalnym obliczem instytucji UE oraz ich tekstów dotyczących tłumaczeń, języka i terminologii. Badanie rozpoczyna się od analizy prawno-językowej tekstów UE pod kątem odniesień do „pracowników”, „reprezentacji pracowników” i „zatrudnienia” w celu zidentyfikowania konkretnych tekstów i organów mających znaczenie dla badania. Takie podejście prowadzi do dwóch kategorii, ujmowanych szeroko: komitetów pracowniczych i związków zawodowych. Instytucje UE poszukują informacji na temat tych organów oraz ich tłumaczeń i ustaleń językowych. Sporządzono także listę stron internetowych dostępnych dla ogółu społeczeństwa, które następnie są badane w ramach profilowania prawno-językowego

    Taking our learning and teaching strategy to the next level through technology enhanced campus development

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    Over the last three years Abertay University has radically evolved its strategy for teaching and supporting learning. This paper outlines Abertay’s journey over the last few years, including the key features of our new pedagogic approach and its impact so far. For example, in 2016 Abertay was the highest ranked modern Scottish University in the National Student Survey (NSS) and shortlisted for the prestigious Times Higher Education “University of the Year” award.In order to further enhance our students’ progression, attainment and employability we have recognized the need to invest further in two key (and related) areas: technology enhanced learning and estate development in order to create a so-called “sticky campus” i.e. somewhere our students will want to come and stay. This has included full implementation of electronic management of assessment (EMA); blended learning; new technology-rich collaborative learning environments and science laboratories which promote richer student-staff interactions and new ways of learning; and a planned complete refurbishment of the University library which will provide a variety of learning environments (formal and informal) from summer 2017.The paper will detail the drivers for these changes; the change management processes involving a staff-student partnership involving management, academic and professional services; successes;challenges; lessons learned and future plans

    Relevance feedback for best match term weighting algorithms in information retrieval

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    Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback
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