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AbstractThousands of Roma were killed in Ukraine by the Nazis and auxiliary police on the spot. There are more than 50,000 Roma in today’s Ukraine, represented by second and third generation decendants of the genocide survivors. The discussion on Roma identity cannot be isolated from the memory of the genocide, which makes the struggle over the past a reflexive landmark that mobilizes the Roma movement. About twenty Roma genocide memorials have been erected in Ukraine during last decade, and in 2016 the national memorial of the Roma genocide was opened in Babi Yar. However, scholars do not have a clear picture of memory narratives and memory practices of the Roma genocide in Ukraine. A comprehensive analysis of the contemporary situation is not possible without an examination of the history and memory of the Roma genocide before 1991.

Recently rediscovered photographs of the remains of thirteen individuals buried in the Sado Valley Mesolithic shell middens of Poças de S. Bento and Arapouco, excavated in 1960 and 1962, show the potential of revisiting excavation archives with new methods. The analysis, which applies the principles of archaeothanatology and is enriched by experimental taphonomic research, confirmed details concerning the treatment of the dead body and provided new insights into the use of burial spaces. Some bodies may have been mummified prior to burial, a phenomenon possibly linked to their curation and transport, highlighting the significance of both the body and the burial place in Mesolithic south-western Portugal.

A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the corpus is multilingual, the same model can be used to learn distributed representations of languages, such that similar languages end up with similar representations. We show that this holds even when the multilingual corpus has been translated into English, by picking up the faint signal left by the source languages. However, just as it is a thorny problem to separate semantic from syntactic similarity in word representations, it is not obvious what type of similarity is captured by language representations. We investigate correlations and causal relationships between language representations learned from translations on one hand, and genetic, geographical, and several levels of structural similarity between languages on the other. Of these, structural similarity is found to correlate most strongly with language representation similarity, whereas genetic relationships—a convenient benchmark used for evaluation in previous work—appears to be a confounding factor. Apart from implications about translation effects, we see this more generally as a case where NLP and linguistic typology can interact and benefit one another.

Iron ore mining in the Norrbotten region of Sweden began in the early years of the twentieth century as a commercially uncertain and even dangerous proposition. But even before it began to generate profits, public debate began over the appropriate role of the state and of private capital (including foreign investors). This included whether iron ore should be exported for profit or retained for future processing in Sweden—even though the technology and infrastructure for such domestic industry did not exist. Tracing the evolution of this debate in the Swedish news media through to the First World War, this paper argues that the revenue generated by exports became more attractive than the promise of future domestic industry because that revenue could underwrite pressing political objectives. Although domestic iron ore processing remained linked to visions of future industrial prosperity, uncertain visions of future prosperity lost appeal as the capacity for export revenues to generate prosperity in the present became more potent.

School in the shadow: Private education in Stockholm 1735. During the early eighteenth century, private education was a more significant sector of the educational market than was public education, regarding the number of students and teachers, the presence of female students and teachers, the social background of the students, and the introduction of a more diverse and modern curriculum. Hitherto, little has been known of the actual scope or general conditions of private education, which has been over-shadowed by studies of public education. The article maps private education through the Stockholm Church Consistory’s (Stockholms stads konsistorium) thorough inventory of private teachers in the capital of Sweden during 1734–36, providing information of both suppliers and consumers within the private sector of the educational market, as well as of the practice and functions of private education in early modern time.

Review of Corpus Linguistics and 17th-century Prostitution: Computational Linguistics and History by Anthony McEnery and Helen Baker (2017)

Educational content recommendation is a cornerstone of AI-enhanced learning. In particular, to facilitate navigating the diverse learning resources available on learning platforms, methods are needed for automatically linking learning materials, e.g., in order to recommend textbook content based on exercises. Such methods are typically based on semantic textual similarity (STS) and the use of embeddings for text representation. However, it remains unclear what types of embeddings should be used for this task. In this study, we carry out an extensive empirical evaluation of embeddings derived from three different types of models: (i) static embeddings trained using a concept-based knowledge graph, (ii) contextual embeddings from a pre-trained language model, and (iii) contextual embeddings from a large language model (LLM). In addition to evaluating the models individually, various ensembles are explored based on different strategies for combining two models in an early vs. late fusion fashion. The evaluation is carried out using digital textbooks in Swedish for three different subjects and two types of exercises. The results show that using contextual embeddings from an LLM leads to superior performance compared to the other models, and that there is no significant improvement when combining these with static embeddings trained using a knowledge graph. When using embeddings derived from a smaller language model, however, it helps to combine them with knowledge graph embeddings. The performance of the best-performing model is high for both types of exercises, resulting in a mean Recall@3 of 0.96 and 0.95 and a mean MRR of 0.87 and 0.86 for quizzes and study questions, respectively, demonstrating the feasibility of using STS based on text embeddings for educational content recommendation. The ability to link digital learning materials in an unsupervised manner—relying only on readily available pre-trained models—facilitates the development of AI-enhanced learning.