Hybrid modelling of intelligence and linguistic factors as predictors of translation quality

Reza Pishghadam, Shaghayegh Shayesteh, Fatemeh Heidari

Abstract


That translators should possess a comprehensive knowledge of the source and target language has long been considered a fundamental prerequisite within translation studies. However, this field seems to overlook the strategic applications of other related areas. Accordingly, the current study particularly sought to adopt an interdisciplinary approach and investigate the quality of forward and backward translation performance based on a pair of complementary viewpoints. From the intelligence-based view, the likely influence of Narrative Intelligence (NI) alongside Verbal Intelligence (VI) was examined. From the linguistic-based view, the L1 and L2 proficiency levels of translators were taken into consideration in order to ultimately determine whether NI, VI or L1/L2 proficiency can predict improved quality of translated texts in both directions. The research involved participation by 231 university students who were selected to complete a set of scales and tests. Structural Equation Modelling (SEM) was utilized to evaluate the correlation between the targeted variables. Upon analysing the data it was found that NI, VI, and L1/L2 proficiency correlate significantly - although differently – with the quality of the translated texts. The results are discussed, and some of their implications are identified and considered in the context of translation studies.


Keywords


Narrative intelligence; Verbal intelligence; Translation; SEM; Language proficiency

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