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2023 Vol. 14 №3

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What is a ‘rare’ language in translation? The experience of distance reading

DOI
10.5922/2225-5346-2023-3-8
Pages
112-124

Abstract

This article examines the perception of ‘rare’ and ‘common’ languages through literary translations. The study is based on the materials from De Bezige Bij Publishing House in the Netherlands, comparing the periods of 2010—2013 and 2020—2023. A significant increase in the role of translators is reflected in the rise of translation share in the publishing house. There is an observed growth in the number of source languages for translation, with a dec­rease in the proportion of English. Translations from French, Italian, German, Scandinavian languages, Portuguese, and Japanese have emerged. A comparison with the Polyandria Rus­sian Publishing House during the period of 2020—2023 reveals common and distinct source lan­guages. Both publishers translate literature into Danish, Finnish, and French to a similar extent. The Russian publishing house represents Norwegian and Japanese to a greater extent, while the Dutch publishing house releases more translations from German, Swedish, Turkish, and Italian. The Russian publisher also includes Icelandic, Albanian, Korean, and Croatian, while the Dutch publisher includes Hebrew, Romanian, and Portuguese. Both publishers en­com­pass a total of 20 source languages, which is a small number compared to the global lin­guistic diversity. Comparing the volumes of source languages also indicates diffe­ren­ces in pre­ferences. Central European languages are chosen in the Netherlands, while Nor­wegian and Ice­landic are favored in Russia. These differences may be influenced by the cost of rights to works, editorial preferences, and translator availability. The analysis results indicate that neither typological similarity between the source language and the target language, nor association with a specific language group, influences the preference for translating books from a particular language. This highlights the importance of sociocultural factors.

Reference

Aiden, E. and Michel, J.-B., 2013. Uncharted: Big Data as a Lens on Human Culture. New York: Riverhead Books, 288 p.

Azarova, N. M. and Bochaver, S. Yu., 2019. From hardship to ease translation. On modern philosophy of translation and target text. Novyi mir [New world], 10, pp. 138—(in Russ.).

Jin Yi., 2019. Corpora of interlingual big data and translation. Vestnik Moskov­skogo universiteta. Ser. 22: Teoriya perevoda [Moscow University Translation Studies Bulletin. Series 22: Translation Theory], 1, pp. 3—14 (in Russ.).

Lutskiv, A. and Popovych, N., 2020. Big data-based approach to automated lin­guis­tic analysis effectiveness. In: 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP). IEEE, pp. 438—443, https://doi.org/10.1109/ DSMP47368.2020.9204057.

Moretti, F., 2005. Graphs, Maps, Trees: Abstract Models for a Literary History. Lon­don; New York.

Paganoni, M. C., 2019. Framing big data: a linguistic and discursive approach. Springer.

Rebora, S. et al., 2021. Digital humanities and digital social reading. Digital Scho­lar­ship in the Humanities, 36 (Supplement 2), pp. ii230-ii250, https://doi.org/10.1093/ llc/fqab020.