Cover not available

Article published In: Translation Spaces
Vol. 11:2 (2022) ► pp.184–212

Get fulltext from our e-platform
JBE logo with link
PDF logo with download linkDownload PDF
EPUB logo with download linkDownload EPUB
authorization required

Creativity in translation

Machine translation as a constraint for literary texts

ORCiD logo with link | University of Surrey | University of Groningen
ORCiD logo with link | University of Groningen
Published online: 28 March 2022
DOI logo with link
https://doi.org/10.1075/ts.21025.gue
Abstract
This article presents the results of a study involving the translation of a short story by Kurt Vonnegut from English to Catalan and Dutch using three modalities: machine-translation (MT), post-editing (PE) and translation without aid (HT). Our aim is to explore creativity, understood to involve novelty and acceptability, from a quantitative perspective. The results show that HT has the highest creativity score, followed by PE, and lastly, MT, and this is unanimous from all reviewers. A neural MT system trained on literary data does not currently have the necessary capabilities for a creative translation; it renders literal solutions to translation problems. More importantly, using MT to post-edit raw output constrains the creativity of translators, resulting in a poorer translation often not fit for publication, according to experts.
Article outline
  • Introduction
  • Related Work
  • Research methodology
    • Source text
    • Machine translation
    • Translation process
    • Translators
    • Review process
    • Measuring creativity
      • Acceptability
      • Units of creative potential (UCP)
      • Novelty
      • Creativity scoring
    • Interviews
  • Results
    • Results on translators’ changes and effort
    • HTER
    • Translation effort according to PET
    • Results from the review
      • Post-task questionnaire
      • Acceptability
      • Creative Shifts
      • Creativity Score
    • Interviews with translators and reviewers
      • 1.MT can only be partially useful in literary translation
      • 2.Creativity is a problem-solving process
      • 3.The proposals act as a constraint to creativity
      • 4.The delicate equilibrium of reviewing
      • 5.The ideal tool for literary translation is human centred
      • 6.The technology or modality impact depends on the type of reader
      • 7.The final translation as a product of many collaborative steps
      • 8.The grim future of translation and technology
  • Conclusions
  • Acknowledgements
  • Notes
  • References
References (49)
References
Aziz, Wilker, Sheila Castilho, and Lucia Specia. 2012. ‘PET: A Tool for Post-Editing and Assessing Machine Translation’. LREC, 3982–87.Google Scholar logo with link to Google Scholar
Bayer-Hohenwarter, Gerrit. 2009. ‘Translational Creativity: How to Measure the Unmeasurable’. In Behind the Mind: Methods, Models and Results in Translation Process Research, edited by Susanne Göpferich, Arnt Lykke Jakobsen, and Inger M. Mees, 371:39–59. Copenhagen: Samfundslitteratur.Google Scholar logo with link to Google Scholar
. 2010. ‘Comparing Translational Creativity Scores of Students and Professionals: Flexible Problem-Solving and/or Fluent Routine Behaviour’. In New Approaches in Translation Process Research, edited by Susanne Göpferich, Fabio Alves, and Inger M. Mees, 831:83–113. 391. Copenhagen: Samfundslitteratur.Google Scholar logo with link to Google Scholar
. 2011. ‘Creative Shifts as a Means of Measuring and Promoting Translational Creativity’. Meta 56 (3): 663–92. Google Scholar logo with link to Google Scholar
. 2012. Translatorische Kreativität: Definition – Messung – Entwicklung. Translations-Wissenschaft. Narr. [URL]
. 2013. ‘Triangulating Translational Creativity Scores’. In Tracks and Treks in Translation Studies: Selected Papers from the EST Congress, Leuven 2010, edited by Catherine Way, Sonia Vandepitte, Reine Meylaerts, and Magdalena Bartłomiejczyk, 1081:63–85. New York, Amsterdam: Benjamins. [URL].
Bayer-Hohenwarter, Gerrit, and Paul Kussmaul. 2020. ‘Translation, Creativity and Cognition’. In The Routledge Handbook of Translation and Cognition, edited by Fabio Alves and Arnt Lykke Jakobsen, 1st ed., 310–25. Routledge. Google Scholar logo with link to Google Scholar
Busselle, Rick, and Helena Bilandzic. 2009. ‘Measuring Narrative Engagement’. Media Psychology 12 (4): 321–47. Google Scholar logo with link to Google Scholar
Daems, Joke. 2021. ‘Wat denken literaire vertalers echt over technologie?’ Tijdschrift over vertalen Filter, 24 January 2021.Google Scholar logo with link to Google Scholar
Fontanet, Mathilde. 2005. ‘Temps de créativité en traduction’. Meta 50 (2): 432–47. Google Scholar logo with link to Google Scholar
Fonteyne, Margot, Arda Tezcan, and Lieve Macken. 2020. ‘Literary Machine Translation under the Magnifying Glass: Assessing the Quality of an NMT-Translated Detective Novel on Document Level’. In Proceedings of the 12th Language Resources and Evaluation Conference, 3790–98. Marseille, France: European Language Resources Association. [URL]
Guerberof-Arenas, Ana, and Antonio Toral. 2020. ‘The Impact of Post-Editing and Machine Translation on Creativity and Reading Experience’. Translation Spaces. John Benjamins. Google Scholar logo with link to Google Scholar
Guilford, Joy Paul. 1950. ‘Creativity’. American Psychologist, no. 5: 444–54. Google Scholar logo with link to Google Scholar
Hakemulder, Jemeljan F. 2004. ‘Foregrounding and Its Effect on Readers’ Perception’. Discourse Processes 38 (2): 193–218. Google Scholar logo with link to Google Scholar
Heiden, Tanja. 2005. ‘Blick in die Black Box: Kreative Momente im Übersetzungsprozess: eine experimentelle Studie mit Translog’. Meta 50 (2): 448–72. Google Scholar logo with link to Google Scholar
Kenny, Dorothy. 2006. ‘Creativity in Translation: Opening up the Corpus-Based Approach’. In Proceedings of the Conference Held on 12th November 2005 in Portsmouth, 72–81. Portsmouth: University of Portsmouth.Google Scholar logo with link to Google Scholar
Kenny, Dorothy, and Marion Winters. 2020. ‘Machine Translation, Ethics and the Literary Translator’s Voice’. Translation Spaces, Fair MT: Towards ethical, sustainable Machine Translation, 9 (1): 123–49. Google Scholar logo with link to Google Scholar
Kolb, Waltraud. 2021. ‘Literary Translation and Post-Editing: Priming Effects and Engagement with the Text’. In Computer-Assisted Literary Translation Conference CALT2021@Swansea, 231. [URL]
Koponen, Maarit, Umut Sulubacak, Kaisa Vitikainen, and Jörg Tiedemann. 2020. ‘MT for Subtitling: User Evaluation of Post-Editing Productivity’. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 115–24. Lisboa, Portugal: European Association for Machine Translation. [URL]
Kussmaul, Paul. 1991. ‘Creativity in the Translation Process: Empirical Approaches’. In Translation Studies: The State of the Art. Proceedings from the First James S. Holmes Symposium on Translation Studies, edited by Kitty M. van Leuven-Zwart and Ton Naaijkens, 91–101. Issue 9 of Approaches to Translation Studies. Amsterdam: Rodopi.Google Scholar logo with link to Google Scholar
. 1995. ‘Creativity in Translation’. In Training the Translator. Benjamins Translation Library, v. 10. Amsterdam: John Benjamins Publishing. Google Scholar logo with link to Google Scholar
Lacruz, Isabel, Michael Denkowski, and Alon Lavie. 2014. ‘Cognitive Demand and Cognitive Effort in Post-Editing’. In Proceedings of the 11th Conference of the Association For Machine Translation in the Americas, Workshop on Post-Editing Technology and Practice (WPTP-3), 73–84. Vancouver, BC, Canada.Google Scholar logo with link to Google Scholar
Lee, Katherine, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018. ‘Hallucinations in Neural Machine Translation’, September. [URL]
Malmkjær, Kirsten. 2019. Translation and Creativity. London and New York: Routledge: Taylor & Francis Group. Google Scholar logo with link to Google Scholar
Mohar, Tjaša, Sara Orthaber, and Tomaž Onič. 2020. ‘Machine Translated Atwood: Utopia or Dystopia?’ ELOPE: English Language Overseas Perspectives and Enquiries 17 (1): 125–41. Google Scholar logo with link to Google Scholar
Moorkens, Joss, Antonio Toral, Sheila Castilho, and Andy Way. 2018. ‘Translators’ Perceptions of Literary Post-Editing Using Statistical and Neural Machine Translation’. Translation Spaces 7 (2): 240–62. Google Scholar logo with link to Google Scholar
Nuland, Sherwin B. 1995. How We Die: Reflections of Life’s Final Chapter, New Edition. 1st edition. New York: Vintage.Google Scholar logo with link to Google Scholar
O’Sullivan, Carol. 2013. ‘Creativity’. In Handbook of Translation Studies, edited by Yves Gambier and Luc van Doorslaer, 41:42–46. Amsterdam: John Benjamins Publishing Company. Google Scholar logo with link to Google Scholar
Rojo, Ana. 2017. ‘The Role of Creativity’. In The Handbook of Translation and Cognition, edited by John W. Schwieter and Aline Ferreira, 1st ed., 350–68. Wiley. Google Scholar logo with link to Google Scholar
Rojo, Ana, and María Ramos Caro. 2016. ‘Can Emotion Stir Translation Skill? Defining the Impact of Positive and Negative Emotions on Translation Performance’. In Reembedding Translation Process Research, edited by Ricardo Muñoz, 1281:107–30. Benjamins Translation Library. Amsterdam: John Benjamins Publishing. Google Scholar logo with link to Google Scholar
Rudowicz, Elisabeth. 2003. ‘Creativity and Culture: A Two Way Interaction’. Scandinavian Journal of Educational Research 47 (3): 273–90. Google Scholar logo with link to Google Scholar
Ruffo, Paola. 2021. ‘In-between Role and Technology: Literary Translators on Navigating the New Socio-Technological Paradigm’. Edinburgh: Heriot-Watt University.Google Scholar logo with link to Google Scholar
Runco, Mark A. 2014. Creativity. Second edition. Academic Press.Google Scholar logo with link to Google Scholar
Runco, Mark A., and Jarret J. Jaeger. 2012. ‘The Standard Definition of Creativity’. Creativity Research Journal 24 (1): 92–96. Google Scholar logo with link to Google Scholar
Şahin, Mehmet, and Sabri Gürses. 2019. ‘Would MT Kill Creativity in Literary Retranslation?’ In Proceedings of the Qualities of Literary Machine Translation, 26–34. Dublin, Ireland: European Association for Machine Translation. [URL]
Sautoy, Marcus Du. 2019. The Creativity Code: How AI Is Learning to Write, Paint and Think. 4th Estate. Google Scholar logo with link to Google Scholar
Sawyer, R. Keith. 2006. Explaining Creativity: The Science of Human Innovation. Oxford ; New York: Oxford University Press.Google Scholar logo with link to Google Scholar
Schmidtke, Dag, and Declan Groves. 2019. ‘Automatic Translation for Software with Safe Velocity’. In Proceedings of Machine Translation Summit XVII Volume 2: Translator, Project and User Tracks, 159–66. Dublin: European Association for Machine Translation. [URL]
Slessor, Stephen. 2020. ‘Tenacious Technophobes or Nascent Technophiles? A Survey of the Technological Practices and Needs of Literary Translators’. Perspectives 28 (2): 238–52. Google Scholar logo with link to Google Scholar
Snover, Matthew, Bonnie Dorr, Richard Schwartz, Linnea Micciulla, and John Makhoul. 2006. ‘A Study of Translation Edit Rate with Targeted Human Annotation’. Proceedings of Association for Machine Translation in the Americas 2001: 223–31.Google Scholar logo with link to Google Scholar
Tezcan, Arda, Joke Daems, and Lieve Macken. 2019. ‘When a “Sport” Is a Person and Other Issues for NMT of Novels’. In, 111.Google Scholar logo with link to Google Scholar
Toral, Antonio, Andreas van Cranenburgh, and Tia Nutters. 2021. ‘Literary-Adapted Machine Translation in a Well-Resourced Language Pair’. In Proceedings of the 7th Conference of The International Association for Translation and Inter- Cultural Studies (IATIS). Barcelona.Google Scholar logo with link to Google Scholar
Toral, Antonio, Antoni Oliver, and Pau Ribas-Bellestín. 2020. ‘Machine Translation of Novels in the Age of Transformer’. In Maschinelle Übersetzung für Übersetzungsprofis, edited by Jörg Porsiel, 276–96. Berlin: BDÜ Fachverlag.Google Scholar logo with link to Google Scholar
Toral, Antonio, Martijn Wieling, and Andy Way. 2018. ‘Post-Editing Effort of a Novel With Statistical and Neural Machine Translation’. Frontiers in Digital Humanities 51: 1–11. Google Scholar logo with link to Google Scholar
Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. ‘Attention Is All You Need’. In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5998–6008. Curran Associates, Inc. [URL]
Vieira, Lucas, Xiaochun Zhang, Roy Yodaule, and Michael Carl. 2020. ‘Machine Translation and Literary Texts: A Network of Possibilities’. Presented at the Creative Translation and Technologies Expert Meeting, University of Surrey, May 29. [URL]
Vonnegut, Kurt. 1999. Bagombo Snuff Box. United States: G. P. Putnam’s Sons.Google Scholar logo with link to Google Scholar
Webster, Rebecca, Margot Fonteyne, Arda Tezcan, Lieve Macken, and Joke Daems. 2020. ‘Gutenberg Goes Neural: Comparing Features of Dutch Human Translations with Raw Neural Machine Translation Outputs in a Corpus of English Literary Classics’. Informatics 7 (3): 32. Google Scholar logo with link to Google Scholar
Cited by (51)

Cited by 51 other publications

Al Rousan, Rafat, Raghad Jaradat & Mona Malkawi
2025. ChatGPT translation vs. human translation: an examination of a literary text. Cogent Social Sciences 11:1 DOI logo
Al-Tarawneh, Alalddin & Mohammed Al-Badawi
2025. Consumer Engagement with Translated Content on Social Media. In From Machine Learning to Artificial Intelligence [Studies in Systems, Decision and Control, 572], ► pp. 901 ff. DOI logo
Bernardini, Silvia & Federico Gaspari
2025. The evolution of Translation (Studies) in the Technocene. Translation and Translanguaging in Multilingual Contexts 11:3 ► pp. 237 ff. DOI logo
Cook, Angela, Angie Knaggs & Jiashun Sun
2025. Exploring the goals of translation education: adding human value to literary translation through deep meaning making and artistry. The Interpreter and Translator Trainer 19:3-4 ► pp. 254 ff. DOI logo
Ed-Dali, Rachid
2025. Assessing DeepSeek R1 and ChatGPT 4.5 in Arabic-English literary translation: performance, challenges, and implications. Cogent Arts & Humanities 12:1 DOI logo
Ferro, Maria João
2025. Corpus Linguistics and Literary Translation. In Exploration of the Intersection of Corpus Linguistics and Language Science, ► pp. 327 ff. DOI logo
Fontanet, Mathilde
2025. La retraduction collective à des fins pédagogiques : le jeu des créativités individuelles. TTR 37:2 ► pp. 145 ff. DOI logo
Kavitha, K., S. Maragathasundari, K. Karthikeyan & C. Swedheetha
2025. 2025 International Conference on Visual Analytics and Data Visualization (ICVADV), ► pp. 1272 ff. DOI logo
Li, Ruichao
2025. 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), ► pp. 1 ff. DOI logo
Li, Xiaoye, Xiangling Wang & Wentian Lai
2025. The Usability of Neural Machine Translation in Creative-Text Post-Editing: Evidence from Users’ Performance and Perception. International Journal of Human–Computer Interaction 41:21 ► pp. 13792 ff. DOI logo
Luo, Jingchun
2025. Unraveling creativity in translation. Translation, Cognition & Behavior DOI logo
Mohammed, Tawffeek A. S.
2025. From Google Translate to ChatGPT. In Role of AI in Translation and Interpretation [Advances in Computational Intelligence and Robotics, ], ► pp. 1 ff. DOI logo
Niu, Shuhua, Weihua Li & Li-Li
2025. The impact of social security systems on public health outcomes: an economic perspective on machine translation applications. Frontiers in Public Health 13 DOI logo
Noriega-Santiáñez, Laura & Gloria Corpas Pastor
2025. Measuring Creative Phraseology in Literature: Machine Translation Systems Versus Large Language Models. Yearbook of Phraseology 16:1 ► pp. 125 ff. DOI logo
Rapi, Muhammad
2025. Interpreting Literature Through an AI Lens. In Role of AI in Producing, Evaluating, and Interpreting Literary Materials [Advances in Computational Intelligence and Robotics, ], ► pp. 97 ff. DOI logo
Walter, Katharina
2025. Literarisches Übersetzen im KI-Zeitalter: Überlegungen zu einer nachhaltigen Markttransformation. Lebende Sprachen 70:1 ► pp. 128 ff. DOI logo
Yao, Xiaofang, Yong-Bin Kang & Anthony McCosker
2025. Missing the human touch?. Translation Spaces DOI logo
Yuke Pitaloka Putri, Syihabuddin Syihabuddin, Farhan Fuadi & Rinaldi Supriadi
2025. Analisis Gaya Bahasa dan Kedalaman Makna Terjemahan Novel Al-Ajnīḥah Al-Mutakassirah: Studi Komparatif antara Terjemahan Ahli dan Terjemahan Quillbot. Jurnal Onoma: Pendidikan, Bahasa, dan Sastra 11:4 ► pp. 5077 ff. DOI logo
Zasiekin , Serhii & Diana Kalishchuk
2025. Can Machines Communicate Psychotrauma? Affective and Cognitive Shifts in AI-Translated Russia-Ukraine War Narratives. PSYCHOLINGUISTICS 38:2 ► pp. 58 ff. DOI logo
Brusasco, Paola & Kristiina Taivalkoski-Shilov
2024. New uses for translation technology? Revising The Job in Italian and Silent Spring in Finnish with the help of translation memory and machine translation. Palimpsestes 38 DOI logo
Carreira, Oliver
2024. Bad business practices in the language services industry. Translation Spaces 13:2 ► pp. 244 ff. DOI logo
Corpas Pastor, Gloria & Laura Noriega-Santiáñez
2024. Human versus Neural Machine Translation Creativity: A Study on Manipulated MWEs in Literature. Information 15:9 ► pp. 530 ff. DOI logo
Dore, Margherita & Giuseppina De Nicola
2024.  Creativity in pivot audiovisual translation. Parasite subtitled and dubbed in Italian . Perspectives 32:5 ► pp. 797 ff. DOI logo
Du, Ying
2024. 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), ► pp. 1 ff. DOI logo
Girletti, Sabrina
2024. Beyond the assembly line: exploring salaried linguists’ satisfaction with translation, revision and PE tasks. Tradumàtica tecnologies de la traducció :22 ► pp. 207 ff. DOI logo
Guerberof-Arenas, Ana & Antonio Toral
2024. To be or not to be. Target. International Journal of Translation Studies 36:2 ► pp. 215 ff. DOI logo
Hansen, Damien
2024. Traduction automatique neuronale et textes littéraires : bibliographie raisonnée. Palimpsestes 38 DOI logo
Hao, Yu, Ke Hu & Anthony Pym
2024. Who’s afraid of literary post-editing? Performances and Reflections of Student Translators. In New Advances in Translation Technology [New Frontiers in Translation Studies, ], ► pp. 263 ff. DOI logo
Jiang, Chunlan & Ying He
2024. 2024 4th International Conference on Digital Society and Intelligent Systems (DSInS), ► pp. 224 ff. DOI logo
Jiménez-Crespo, Miguel A.
2024. Transcreation in Translation and the Age of Human-Centred AI: Focusing on “Human” Creative Touch. In New Perspectives in Media Translation, ► pp. 309 ff. DOI logo
Jiménez-Crespo, Miguel A.
2025. “If students translate like a robot … ” or how research on human-centered AI and intelligence augmentation can help realign translation education. The Interpreter and Translator Trainer 19:3-4 ► pp. 277 ff. DOI logo
Jiménez-Crespo, Miguel A.
2025. Human-Centered AI and the Future of Translation Technologies: What Professionals Think About Control and Autonomy in the AI Era. Information 16:5 ► pp. 387 ff. DOI logo
Kayadibinli, Ezgi & Göksenin Abdal
2024. Edebi Metni Makine Çevirmenden Okumak: Çevirmen Okurun Makine Çevirisi Alımlaması. Karamanoğlu Mehmetbey Üniversitesi Uluslararası Filoloji ve Çeviribilim Dergisi 6:1 ► pp. 127 ff. DOI logo
Kodura, Małgorzata
2024. Rozwijanie kreatywności studentów na zajęciach z przekładu w epoce sztucznej inteligencji . Półrocznik Językoznawczy Tertium 9:1 ► pp. 274 ff. DOI logo
Liu, Jia
2024. 2024 Second International Conference on Data Science and Information System (ICDSIS), ► pp. 1 ff. DOI logo
Liu, Zhijia
2024. 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS), ► pp. 930 ff. DOI logo
Moorkens, Joss
2024. ‘I am not a number’: on quantification and algorithmic norms in translation. Perspectives 32:3 ► pp. 477 ff. DOI logo
Moorkens, Joss
2025. The machine translator’s visibility. Translation Spaces DOI logo
O’Brien, Sharon
2024. Human-Centered augmented translation: against antagonistic dualisms. Perspectives 32:3 ► pp. 391 ff. DOI logo
Rivas Ginel, María Isabel & Joss Moorkens
2024. A year of ChatGPT: translators’ attitudes and degree of adoption. Tradumàtica tecnologies de la traducció :22 ► pp. 258 ff. DOI logo
Rădoi, Alina
2024. Literal or Literary Machine Translation? Case Study: Winnie-The-Pooh. Romanian Journal of English Studies 21:1 ► pp. 142 ff. DOI logo
Sanz-Villar, Zuriñe
2024. Los fraseologismos en el aula de prácticas de traducción: estudio de caso sobre el impacto de los traductores automáticos. Yearbook of Phraseology 15:1 ► pp. 33 ff. DOI logo
van Egdom, Gys-Walt
2024. Bridging Linguistic Divides? A Critical Exploration of Machine Translation’s Role in Fostering Cross-Cultural Accessibility in Literature. Íkala, Revista de Lenguaje y Cultura 29:3 DOI logo
Wang, Xiaodong & Cuicui Li
2024. 2024 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC), ► pp. 243 ff. DOI logo
Zhang, Liyan & Hailong Zhao
2024. 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), ► pp. 1 ff. DOI logo
Zhou, Yanjun & Shuling Zhou
2024. 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), ► pp. 1 ff. DOI logo
DOLMACİ, Maide & Halil İbrahim BALKUL
2023. Yabancı Dil Öğretiminde Makine Çevirisi Kullanımı: Yabancı Dil Eğitmenlerinin Görüşleri Üzerine Görgül Bir Çalışma. Karamanoğlu Mehmetbey Üniversitesi Uluslararası Filoloji ve Çeviribilim Dergisi 5:2 ► pp. 85 ff. DOI logo
Yang, Jing
2023. ECBTNet: English-Foreign Chinese intelligent translation via multi-subspace attention and hyperbolic tangent LSTM. Neural Computing and Applications 35:36 ► pp. 25001 ff. DOI logo
Łoboda, Krzysztof & Olga Mastela
2023. Machine translation and culture-bound texts in translator education: a pilot study. The Interpreter and Translator Trainer 17:3 ► pp. 503 ff. DOI logo
Liang, Lisi
2022. How COVID-19 changes the way we approach pedagogy for translation. The Journal of Internationalization and Localization 9:2 ► pp. 230 ff. DOI logo
Kobyakova, Iryna & Svitlana Shvachko
2016. TEACHING TRANSLATION: OBJECTIVES AND METHODS. Advanced Education :5 ► pp. 9 ff. DOI logo

This list is based on CrossRef data as of 6 december 2025. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.

Mobile Menu Logo with link to supplementary files background Layer 1 prag Twitter_Logo_Blue