Chapter 14. Conclusions

Mori, Laura

doi:10.1075/scl.86.14mor

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…So far, the Eurolect Observatory Project allowed us to depict some cross-language trends shared by several Eurolects related to background factors that shape EU law: the multilingual law-making process, the multi-source translation practice and the role of English as an institutional lingua franca. Nonetheless diverging trends were also reported and in Mori (2018d) an interpretative hypothesis was envisaged. According to this, clustering of Eurolects could be put in correlation with potentially relevant external variables: a. time since accession to the EU 19 b. sociolinguistic profile c. legal culture d. model language e. genetic constraints Extralinguistic variables (from variable A to D) could help to explain the reasons for divergence and for language clustering, regardless of any linguistic relationship (variable E).…”

Section: A Correlational Hypothesismentioning

confidence: 94%

“…Europeisms occur in Eurolects to refer to concepts, institutions, policies, principles and practices that are specific to the EU environment. Their over-representation in EU law was reported in Mori (2018d) especially for EU noun phrases (English, French, Greek, Italian, Latvian, Polish and Spanish) and Semantic Europeisms (Finnish, German, Greek, Italian, Latvian, Maltese, Polish, Spanish).…”

Section: Feature Group 1: Eu-rooted Phenomenamentioning

confidence: 99%

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Mapping Eurolects

Mori

Szmrecsanyi

2020

LiC

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Based on the description of EU legislative varieties covering EU directives and their national laws of implementation in 11 languages, we are interested in the extent to which Eurolects are similar to each other, above and beyond trivial genealogical similarities. We thus utilise a variation-oriented aggregative analysis technique to address these questions: (a) What is the precise extent to which Eurolects are similar to each other? (b) Are similarities predicted by extra-linguistic affinities? (c) Do factors such as EU accession dates and language policy play a role in shaping the Eurolect clusters? Our methodology starts out from a meticulously catalogued list of corpus-based lexical and grammatical features. Through the observed presence or absence of these features, we calculate in a second step the aggregate linguistic distances between all of the Eurolects. Finally, in step three, we use a well-established technique, Multidimensional Scaling, to visualize and interpret the Eurolect landscape.

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Section: A Correlational Hypothesismentioning

confidence: 94%

Section: Feature Group 1: Eu-rooted Phenomenamentioning

confidence: 99%