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Edge Hill University

Edge Hill University. UCCTS 2010, 27.-29.7.2010. The dilemma between corpus statistics and reception of a text: An analysis of foreignising and domesticating elements of translations Hannu Kemppanen, Jukka Mäkisalo & Grigory Gurin University of Eastern Finland.

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Edge Hill University

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  1. Edge Hill University UCCTS 2010, 27.-29.7.2010

  2. The dilemma between corpus statistics and reception of a text: An analysis of foreignising and domesticating elements of translations Hannu Kemppanen, Jukka Mäkisalo & Grigory Gurin University of Eastern Finland

  3. Background: The concepts of foreignisationand domestication • Venuti (1995) • criticism (Tymoczko 2000, Boyden 2006) • obscurity of the notions • dichotomy • Attempts to concretise the concepts - e.g. Pedersen 2005

  4. Background: results of earlierstudies • keyword studies • comparing translated and non-translated texts • keywords as untypical, foreign elements (Kemppanen 2004, 2008) • study where statistical features of translated texts were compared with the results of an evaluation test (Kemppanen and Mäkisalo 2010) • no correlation between the statistical features and the results of the test • subjectivity in ranking translations • individual words/phrases and foreign elements draw subjects’ attention

  5. Objective and methods of the study • possible correlation between statistical features of the texts and the results of the evaluation test • a corpus-based analysis of non-fiction translations (Russian-Finnish)and non-translations - foreignising/domesticating features of translated vs. non-translated texts • an evaluation test - foreignising/domesticating features of translated vs. non-translated texts (cf. the former study: different subjects, different reference corpus) • foreignising/domesticating features of translated vs. non-translated texts foreignising/domesticating features of translated vs. non-translated texts *

  6. Corpus-basedanalysis • Keywords: • number of keywords • keyness maximum value • keyness mean value • Other features: • type/token ratio • mean length of sentences • k

  7. RESULTS: STATISTICAL FEATURES • There are only weak statistical correlations between some of the features. • On one hand, type/token ratio correlates to some extent reversely with mean keyness value (Pearson p = 0,62). • On the other hand, weak correlation between a high number of keywords and high keyness maximum value (p = 0,51). • However, overall, when history texts are compared to newspaper texts, various statistical features do not correlate with each other.

  8. Questionnaire: evaluation test • A questionnaire for ranking the texts according to (subjective) impression of domestication/ foreignisation. • Evaluating extracts (1000 words) of four Russian–Finnish translations and two non-translations on Finnish political history on a scale 1–5 (domestic–foreign). • In addition, naming at least one foreignising or domesticating feature in each text • Pilot: five subjects, translation students (earlier six translation trainers)

  9. Results: evaluation test • Four Russian-Finnish translations and two non-translated Finnish history texts were ranked according to the median of evaluations • The ranges of evaluations between the texts varied a lot, highlighting the difference between translations and non-translations.

  10. Grouping according to the range of D/F into three: 1) partially domestic texts, 2) partially foreignised texts and 3) clearly foreignised texts EvalMedRange Tr/Non-tr • Tarkka 2 1 – 3 2/3 • Apunen 3 1 – 3 1/4 • Bartenjev 3 2 – 5 3/2 • Holodkovskij 4 3 – 4 4/1 • Komissarov 4 4 – 5 5/0 • Baryshnikov 4 4 – 5 5/0

  11. Results: statistical features and the evaluation test • The ranking of evaluation correlates only weakly with sentence length (p = 0,59). • With various keyness values or TTR, the evaluation test has no correlation.

  12. Comments on foreignisation/domestication • Sentence structure  foreign (10) • Word order  foreign (2) • Phrases/Collocations  foreign (4) • Phrases/Collocations  domestic (colourful expressions) (3) • Individual words  foreign (4) (adjectives)

  13. Attitudinal features  foreign (10) (NB: foreign point of view in a fluent text, one comment) • Attitudinal features  domestic (2) (point of view, neutrality) • Fluency/style  domestic (7) (fluent/good Finnish) • Non-fluency/style  foreign (1) • Orthography  foreign (1) • Explanations foreign (translation) (1)

  14. Conclusions and discussion • results of the study support the earlier empirical findings • for the most part, statistical features of the texts do not correlate with the results of the evaluation test • various statistical features retrieved from the corpus analysis are not in line with each other • new findings - the evaluation test differentiates non-translated texts from translated texts, and furthermore, more detailed sub-groups of translations

  15. Conclusions and discussion • Can a translationberecognised on the grounds of the analysedfeatures? • on the grounds of statisticalfeatures – NO • on the grounds of the evaluationtest – YES • Categorisation of texts into translated and non-translatedtexts, and naming of textfeatures in a qualitativestudysuggestthatforeigness of a text is a marked feature

  16. Thank you! Questions? Comments?

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