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'I am not a number': on quantification and algorithmic norms in translation.

Numbers and measurements enable transactions and communication in translation in ways that are helpful and indisputably necessary. However, as deployment of quantification and mathematisation has become more complex and opaque, it is important to interrogate the validity of measures and predictions,...

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Published in:Perspectives: Studies in Translatology Vol. 32; no. 3; pp. 477 - 493
Main Author: Moorkens, Joss
Format: Article
Published: Taylor & Francis Ltd Jun2024
Online Access:View this record in EBSCOhost
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        atl: 'I am not a number': on quantification and algorithmic norms in translation.
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        au: Moorkens, Joss
        affil: Applied Language and Intercultural Studies, Dublin City University, Dublin, Ireland
      su: Reverse engineering
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        subj: Reverse engineering
      keyword:
        algorithms
        mathematisation
        translation norms
        Translation technology
      ab: Numbers and measurements enable transactions and communication in translation in ways that are helpful and indisputably necessary. However, as deployment of quantification and mathematisation has become more complex and opaque, it is important to interrogate the validity of measures and predictions, especially if they are to be used as a basis for action. This article takes a critical look at the various types of quantification and mathematisation used in translation and considers the effects of these on translators working in highly technologized workflows. It introduces the concept of algorithmic norms, whereby translators feel pressured to reverse engineer and conform to the demands of algorithmic management.
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    language: English
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      custom: Copyright of Perspectives: Studies in Translatology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use.
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