Europe - space for transcultural existence?

Europe - Space for Transcultural Existence? is the first volume of the new series, Studies in Euroculture, published by Göttingen University Press. The series derives its name from the Erasmus Mundus Master of Excellence Euroculture: Europe in the Wider World, a two year programme offered by a consortium of eight European universities in collaboration with four partner universities outside Europe. This master highlights regional, national and supranational dimensions of the European democratic development; mobility, migration and inter-, multi- and transculturality. The impact of culture is understood as an element of political and social development within Europe. The articles published here explore the field of Euroculture in its different elements: it includes topics such as cosmopolitanism, cultural memory and traumatic past(s), colonial heritage, democratization and Europeanization as well as the concept of (European) identity in various disciplinary contexts such as law and the social sciences. In which way have Europeanization and Globalization influenced life in Europe more specifically? To what extent have people in Europe turned ‘transcultural’? The ‘trans’ is understood as indicator of an overlapping mix of cultures that does not allow for the construction of sharp differentiations. It is explored in topics such as (im)migration and integration, as well as cultural products and lifestyle. The present economic crisis and debt crisis have led, as side-result, to a public attack on the open, cosmopolitan outlook of Europe. The values of the multicultural and civil society and the idea of a people’s Europe have become debatable. This volume offers food for thought and critical reflection

The Normative Order of the Internet: A Theory of Rule and Regulation Online

There is order on the internet, but how has this order emerged and what challenges will threaten and shape its future? This study shows how a legitimate order of norms has emerged online, through both national and international legal systems. It establishes the emergence of a normative order of the internet, an order which explains and justifies processes of online rule and regulation. This order integrates norms at three different levels (regional, national, international), of two types (privately and publicly authored), and of different character (from ius cogens to technical standards). The author assesses their internal coherence, their consonance with other order norms and their consistency with the order's finality. The normative order of the internet is based on and produces a liquefied system characterized by self-learning normativity. In light of the importance of the socio-communicative online space, this is a book for anyone interested in understanding the contemporary development of the internet.

Decoding Legalese Without Borders: Multilingual Evaluation of Language Models on Long Legal Texts

Pretrained transformers have sparked an explosion of research in the field of Natural Language Processing (NLP). Scaling up language models based on the transformer architecture in terms of size, compute, and data led to impressive emergent capabilities that were considered unattainable in such a brief span, a mere three years ago, prior to the launch of GPT-3. These advances catapulted the previously niche field of legal NLP into the mainstream, at the latest, with GPT-4 passing the bar. Many products based on GPT-4 and other large language models are entering the market at an increasing pace, many of those targeting the legal field. This dissertation makes contributions in two key areas within Natural Language Processing (NLP) focused on legal text: resource curation and detailed model analysis. First, we curate an extensive set of multilingual legal datasets, train a variety of language models on these, and establish comprehensive benchmarks for evaluating Large Language Models (LLMs) in the legal domain. Second, we conduct a multidimensional analysis of model performance, focusing on metrics like explainability and calibration in the context of Legal Judgment Prediction. We introduce novel evaluation frameworks and find that while our trained models exhibit high performance and better calibration than human experts, they do not necessarily offer improved explainability. Furthermore, we investigate the feasibility of re-identification in anonymized legal texts, concluding that large-scale re-identification using LLMs is currently unfeasible. For future work, we propose exploring domain adaptation and instruction tuning to enhance language model performance on legal benchmarks, while also advocating for a detailed examination of dataset overlaps and model interpretability. Additionally, we emphasize the need for dataset extension to unexplored legal tasks and underrepresented jurisdictions, aiming for a more comprehensive coverage of the global legal landscape in NLP resources