Defining ‘labour’ and ‘work’ on translation crowdsourcing platforms via motivational and affective theory: A theoretical-conceptual framework
To date, relatively little effort has been put into establishing clear definitions of ‘labour’ and ‘work’ in Translation Studies. The two concepts are, however, gaining more attention in the context of the precarisation of the translation profession brought about by neural machine translation specifically and (generative) artificial intelligence more generally. There is no better illustration of this precarisation than the platforms offering paid translation crowdsourcing and using machine translation and AI. This article examines whether translators working on these platforms consider these activities labour or work based on their motivation and affective states. Its key objective is to develop a theoretical-conceptual framework for such an investigation and to attempt to apply that framework to the few existing empirical studies undertaken among translators working in the translation platform industry.
- labour,
- work,
- motivation,
- affects,
- theoretical-conceptual framework
Publication history
Table of contents
Translation Studies has only recently started to consider the work- and labour-related dimensions of translation and interpreting. Like cultural, communication and media studies, it too had hitherto neglected the question, or rather concept, of labour and failed to investigate the structural labour conditions of translators and interpreters (Fuchs 2014). The recent endeavour to put labour under the radar of Translation Studies researchers may be traced back to the increasing precarisation of translation and interpreting and their dislocation to the digital space and onto platforms — a development that raises vital questions around the issue of labour and undermines the romanticising of translators as creative and artistic actors untouched by capitalist forces and pressures (Zwischenberger and Alfer 2022; Giustini 2024).
Funding
Open Access publication of this article was funded through a Transformative Agreement with University of Vienna.