Machine translation in society: insights from UK users
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Apple. (2021a). App store preview—Google Translate. Retrieved December 7, 2021, from https://apps.apple.com/gb/app/google-translate/id414706506.
Apple. (2021b). App store preview—Microsoft Translator. Retrieved February 1, 2022, from https://apps.apple.com/gb/app/microsoft-translator/id1018949559.
Asscher, O., & Glikson, E. (2021). Human evaluations of machine translation in an ethically charged situation. New Media & Society. https://doi.org/10.1177/14614448211018833
Bowker, L. (2020a). Machine translation literacy instruction for international business students and business English instructors. Journal of Business & Finance Librarianship, 25(1–2), 25–43. https://doi.org/10.1080/08963568.2020.1794739
Bowker, L. (2020b). Fit-for-purpose translation. In M. O’Hagan (Ed.), The Routledge handbook of translation technology (pp. 453–468). Routledge.
Bowker, L., & Buitrago Ciro, J. (2019). Machine translation and global research: Towards improved machine translation literacy in the scholarly community. Emerald Publishing Limited.
Bradley, P. (2018). Bots and data quality on crowdsourcing platforms. Prolific Blog. https://blog.prolific.co/bots-and-data-quality-on-crowdsourcing-platforms/
Campbell-Cree, A. (2017). Which Foreign languages will be most important for the UK post-Brexit? British Council. Retrieved August 6, 2021, from https://www.britishcouncil.org/research-policy-insight/insight-articles/which-foreign-language.
Canfora, C., & Ottmann, A. (2020). Risks in neural machine translation. Translation Spaces, 9(1), 58–77. https://doi.org/10.1075/ts.00021.can
Danet, B., & Herring, S. C. (2017). Introduction: The multilingual Internet. Journal of Computer-Mediated Communication, 9(1), JCMC9110. https://doi.org/10.1111/j.1083-6101.2003.tb00354.x
Esteve, A. (2017). The business of personal data: Google, Facebook, and privacy issues in the EU and the USA. International Data Privacy Law, 7(1), 36–47
Gaspari, F. (2007). The role of online MT in webpage translation [PhD Thesis, University of Manchester]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.840&rep=rep1&type=pdf
Google. (n.d.). Google Translate: About. Google. Retrieved July 19, 2021, from https://translate.google.com/intl/en/about/.
Gottschalk, C. M., & Thompson, M. S. (1959). Report on the state of machine translation in the United States. Yehoshua Bar Hillel. Technical Report No. 1. Prepared for the U.S. Office of Naval Research, Information Systems Branch, Jerusalem, Israel, 1959 (available as PB151746 from Office of Technical Services, U.S. Dept. of Commerce, Washington 25, D.C.). 48 pp. + appendixes. $2.25. Science, 130(3383), 1185–1185. https://doi.org/10.1126/science.130.3383.1185-b
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F., Wattenberg, M., Corrado, G., Hughes, M., & Dean, J. (2017). Google’s multilingual neural machine translation system: Enabling zero-shot translation. arXiv:1611.04558.
Kirilenko, A. P., & Stepchenkova, S. (2016). Inter-coder agreement in one-to-many classification: Fuzzy Kappa. PLoS ONE, 11(3), e0149787. https://doi.org/10.1371/journal.pone.0149787.
Klein, H. K., & Kleinman, D. L. (2002). The social construction of technology: Structural considerations. Science, Technology, & Human Values, 27(1), 28–52. http://www.jstor.org/stable/690274
Lawson, V., & Vasconcellos, M. (1994). Forty ways to skin a cat: Users report on machine translation. Aslib Proceedings, 46(3), 83–87. https://doi.org/10.1108/eb051348.
Microsoft. (2021). Microsoft Translator. Retrieved July, 20, 2021 from https://www.microsoft.com/en-us/translator/languages/.
Nitzke, J., Hansen-Schirra, S., & Canfora, C. (2019). Risk management and post-editing competence. The Journal of Specialised Translation, 31, 239–259
Nord, C. (2014). Translating as a purposeful activity: Functionalist approaches explained. Routledge.
Nurminen, M., & Papula, N. (2018). Gist MT users: A snapshot of the use and users of one online MT tool. In J. A. Pérez-Ortiz, F. Sánchez-Martínez, M. Esplà-Gomis, M. Popović, C. Rico, A. Martins, J. Van den Bogaert, & M. L. Forcada (Eds.), Proceedings of the 21st annual conference of the European Association for machine translation, Alicant, Spain, May 2018 (pp. 199–208). European Association for Machine Translation. http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf
Ofcom. (2019). Online nation. 2019 report. The office of communications. Retrieved from https://www.ofcom.org.uk/__data/assets/pdf_file/0024/149253/online-nation-summary.pdf.
O’Hagan, M. (2009). Evolution of user-generated translation: Fansubs, translation hacking and crowdsourcing. The Journal of Internationalization and Localization, 1(1), 94–121
Olohan, M. (2011). Translators and translation technology: The dance of agency. Translation Studies, 4(3), 342–357. https://doi.org/10.1080/14781700.2011.589656
Olohan, M. (2017). Technology, translation and society: A constructivist, critical theory approach. Target, 29(2), 264–283
ONS (2020a). Estimates of the population for the UK, England and Wales, Scotland and Northern Ireland (MYE2—Persons, mid-2019: April 2020 local authority district codes edition). Office for National Statistics. Retrieved February 1, 2022, from https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland.
ONS (2020b). Education and training statistics for the UK. Office for National Statistics. Retrived February 1, 2022, from https://explore-education-statistics.service.gov.uk/find-statistics/education-and-training-statistics-for-the-uk/2020.
Oudshoorn, N., & Pinch, T. (2003). Introduction. In N. Oudshoorn & T. Pinch (Eds.), How users matter: The co-construction of users and technology. The MIT Press. https://doi.org/10.7551/mitpress/3592.003.0002.
Pérez-Sabater, C., Peña-Martínez, G., Turney, E., & Montero-Fleta, B. (2008). A spoken genre gets written: Online football commentaries in English, French, and Spanish. Written Communication, 25(2), 235–261. https://doi.org/10.1177/0741088307313174
Pickering, A. (2010). Material culture and the dance of agency. In D. Hicks & M. C. Beaudry (Eds.), The Oxford handbook of material culture studies (pp. 191–208). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199218714.013.0007.
Pinch, T. J., & Bijker, W. E. (1987). The social construction of facts and artifacts: Or how the sociology of science and the sociology of technology might benefit each other. In W. E. Bijker, T. P. Hughes, & T. J. Pinch (Eds.), The social construction of technological systems: New directions in the sociology and history of technology. MIT Press.
Pitman, J. (2021). Google Translate: One billion installs, one billion stories. The Keyword. https://blog.google/products/translate/one-billion-installs/.
Pituxcoosuvarn, M., & Ishida, T. (2018). Multilingual communication via best-balanced machine translation. New Generation Computing, 36(4), 349–364. https://doi.org/10.1007/s00354-018-0041-7
Prolific Team. (2019). Representative samples FAQ. Prolific.co. Retrieved July 2, 2021, from https://researcher-help.prolific.co/hc/en-gb/articles/360019238413-Representative-Samples-FAQ.
Ramati, I., & Pinchevski, A. (2018). Uniform multilingualism: A media genealogy of Google Translate. New Media & Society, 20(7), 2550–2565. https://doi.org/10.1177/1461444817726951
Robertson, S., Deng, W. H., Gebru, T., Mitchell, M., Liebling, D. J., Lahav, M., Heller, K., Díaz, M., Bengio, S., & Salehi, N. (2021). Three directions for the design of human-centered machine translation. Google Research. https://storage.googleapis.com/pub-tools-public-publicationdata/pdf/1bb66ff36a5eb4650a76a3d05ea57e09c0203366.pdf
Sindoni, M. G. (2013). Spoken and written discourse in online interactions: A multimodal approach. Routledge.
Specia, L., Blain, F., Logacheva, V., Astudillo, R., & Martins, A. (2018). Findings of the WMT 2018 Shared Task on Quality Estimation. In Proceedings of the Third Conference on Machine Translation (WMT) (Vol. 2, pp. 689–709). Association for Computational Linguistics. http://aclweb.org/anthology/W18-6451
Tinsley, J., & Shterionov, D. (2019). Foreword by the user track program chairs. In B. Haddow, R. Sennrich, J. Tinsley, D. Shterionov, C. Rico, F. Gaspari, & M. L. Forcada (Eds.), Proceedings of machine translation summit XVII, Volume 2: Translator, project and user tracks (pp. n.p.). European Association for Machine Translation
van Dijk, J. (2012). The Network society (3rd ed.). Sage.
Vieira, L. N., O’Hagan, M., & O’Sullivan, C. (2020). Understanding the societal impacts of machine translation: A critical review of the literature on medical and legal use cases. Information, Communication & Society, 24(11), 1515–1532. https://doi.org/10.1080/1369118X.2020.1776370
Way, A. (2013). Traditional and emerging use-cases for machine translation. Paper presented at Translating and the Computer 35. https://www.computing.dcu.ie/~away/PUBS/2013/Way_ASLIB_2013.pdf
Way, A. (2018). Quality expectations of machine translation. In J. Moorkens, S. Castilho, F. Gaspari, & S. Doherty (Eds.), Translation quality assessment (pp. 159–178). Springer.
Wolfe, R. (2021). Special issue: Sign language translation and avatar technology. Machine Translation, 35(3), 301–304. https://doi.org/10.1007/s10590-021-09270-4