Abstract
This research fills the gap concerning the lack of translation experts in the food sector by creating and assessing a novel cooperative university-industry model of training guided by translation sociology and inters miotic theory. This model describes a comprehensive pedagogical scenario which includes cooperative curriculum design in food science, compliance documents, and cross-cultural promotion. Through collaboration between academic institutions and food industry enterprises, the curriculum was implemented. Mixed-methods evaluation using quantitative and qualitative performance metrics and stakeholder evaluation within three cohorts (total n=186) revealed substantial gains in accuracy of technical translation - in the post-training phase, participants made an average of 4.8 errors per 1000 words compared to a baseline of 23.4, alongside mastery of food terminology which rose to 91.7% post-training from a baseline of 43.2%, improvement in employment outcomes to a 96.8% placement rate compared to the industry average of 74.5%. The training model’s project-based approach supports interaction with a wide range of texts, including but not limited to technical documents and multicultural marketing essays, through active real-world-aligned multi-evaluator frameworks out of industry performance benchmarks. Results show revolutionary enhancement in graduate employability and accelerated professional competency development, reinforced industry-academia collaboration, with diminished gap between men and women across the field of technical translation. This establishes a replicable model geared towards specialized translator training deficiencies spanning multiple professions while promoting diverse representation in translation education.
References
[1] Abdal G, Yaman B. Mediatorship in the clash of hegemonic and counter publics: the curious case of Heartstopper in Turkey. Transl Interpret Stud. 2023;18(2):280-299. doi:10.1075/tis.22044.abd
[2] Alzamila AM. Translation competence between industry and academia in Saudi Arabia: job descriptions vs students’ perceptions. Interpreter Transl Train. 2024;18(3):442-464. doi:10.1080/1750399X.2024.2328855
[3] Bartłomiejczyk M, Pöllabauer S, Straczek-Helios V. Activist interpreting in abortion clinics: emotional challenges and self-care strategies. Transl Interpret Stud. 2024;19(3):405-431. doi:10.1075/tis.23036.bar
[4] European Commission. European Master’s in Translation - EMT Competence Framework 2022. Brussels: European Commission; 2022. Available from: https://commission.europa.eu/system/files/2022-11/emt_competence_fwk_2022_en.pdf
[5] Giannakopoulou V. Introduction: intersemiotic translation as adaptation. Adaptation. 2024;12(3):199-205. doi:10.1093/adaptation/apae007
[6] Hassoun A, Aït-Kaddour A, Abu-Mahfouz AM, et al. From Food Industry 4.0 to Food Industry 5.0: identifying technological enablers and potential future applications in the food sector. Compr Rev Food Sci Food Saf. 2024;23(2):e70040. doi:10.1111/1541-4337.13300
[7] Koka NA, Alqahtani SMS, Ahmad J, Jan N, Khasawneh M. Bridging the gap between academic translation programs and industry demands: stakeholders’ perspectives on future directions. Int J Transl Interpret Res. Published online May 9, 2024.
[8] Krimpas P. Olimpia G. Loddo: Intersemiotic Legal Translation. Int J Leg Discourse. 2024;9(1):205-215. doi:10.1515/ijld-2023-2069
[9] Li B. Political motivation in media interpreting: 2020 US presidential debates livestreamed by two Taiwanese TV stations. Transl Interpret Stud. 2024;19(3):456-476. doi:10.1075/tis.23025.li
[10] Luo X. Big Translation and Cultural Memory: integration and enlightenment. Asia Pac Transl Intercult Stud. 2024;11(1):1-8. doi:10.1080/23306343.2024.2312280
[11] Pięta H, Maia RB, Torres-Simón E. Indirect translation explained. Int J Transl Interpret Res. 2024;116201. doi:10.1080/0907676X.2024.2349087
[12] Pym, Anthony and Torres-Simón, Ester. “Is automation changing the translation profession?” International Journal of the Sociology of Language, vol. 2021, no. 270, 2021, pp. 39-57. https://doi.org/10.1515/ijsl-2020-0015
[13] Wang X, Zhang Y. Personalized translator training in the era of digital intelligence: opportunities, challenges, and prospects. Heliyon. 2024;10(19):e15385. doi:10.1016/j.heliyon.2024.e15385
[14] Yang Y, Wang L. Performance and perception: machine translation post-editing in Chinese-English news translation by novice translators. Humanit Soc Sci Commun. 2023;10:798. doi:10.1057/s41599-023-02200-0
[15] Zhang S, Zhuang Y, Chang L. Cultural mediation in crisis translation: a snapshot of the citizen translator in China’s Greater Bay Area during the COVID-19 pandemic. Transl Interpret Stud. 2023;18(2):301-324. doi:10.1075/tis.22045.zha

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