Moorkens, Joss ORCID: 0000-0003-0766-0071 (2020) “A tiny cog in a large machine”: Digital Taylorism in the Translation Industry. Translation Spaces, 9 (1). pp. 12-34. ISSN 2211-3711
Abstract
Translators have worked with the assistance of computers for many years, usually translating whole texts, divided into segments but in sequential order. In order to maximise efficiency and inspired by similar moves in the tech industry and predictions for Industry 4.0, large translation companies have begun to break tasks down into smaller chunks and to rigidly define and monitor translation processes. This is particularly true of platform-mediated work, highly collaborative workflows, and multimedia work that requires near-live turnaround times. This article considers such workflows in the context of measures of job satisfaction and discussion of sustainable work systems, proposing that companies prioritise long-term returns and attempt to balance the needs of all stakeholders in a translation process. Translators and translator trainers also have a role to play in achieving this balance.
Metadata
Item Type: | Article (Published) |
---|---|
Refereed: | Yes |
Uncontrolled Keywords: | business ethics; digital Taylorism; fair MT; labour sociology; scientific management; sustainable work systems and translation industry |
Subjects: | Computer Science > Artificial intelligence Computer Science > Machine translating Humanities > Translating and interpreting |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Humanities and Social Science > School of Applied Language and Intercultural Studies Research Institutes and Centres > ADAPT |
Publisher: | John Benjamins |
Official URL: | http://dx.doi.org/10.1075/ts.00019.moo |
Copyright Information: | © John Benjamins Publishing Company |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | This work was supported by the ADAPT Centre for Digital Content Technology at Dublin City University, funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is cofunded under the European Regional Development Fund. |
ID Code: | 24968 |
Deposited On: | 02 Sep 2020 16:31 by Joss Moorkens . Last Modified 02 Sep 2020 16:31 |
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