Multilingualism in Scholarly Communication: How Far Can Technology Take Us and What Else Can We Do?
Bibliographic record
Abstract
Disparities created by the use of English as the key language for scholarly publishing are becoming increasingly clear in many disciplines.Tatsuya Amano et al. (2023) surveyed environmental scientists around the globe and found that it takes non-native speakers of English substantially more time, effort, and money to read and write articles in English.Jacky Deng and Alison Flynn (2023, 1529) interviewed non-Anglophone graduate students in chemistry and learned that for many, communicating research in English is "their most pervasive challenge."In the field of digital humanities (DH), Puthiya Purayil Sneha (2022, 15) emphasizes that "the prevalent global discourse around DH is largely Anglocentric," while Roopika Risam (2018, 79) points out that this often leads to "centering epistemologies and ontologies of the Global North, namely the U.S. and western Europe, which in turn decenters those of Indigenous communities and the Global South."Scholars who publish in languages other than English are cited less often (Di Bitetti and Ferreras 2017), and there is "a persistent lack of international representation on editorial boards" (Espin et al. 2017).But while the problems stemming from the use of a single language for science are becoming ever clearer, the path forward is less obvious.For instance, if all scholars publish in their own language, how will others evaluate, discover, or read their work?Some are pinning their hopes on technologies, such as automatic translation tools (e.g., Google Translate) and tools based on large language models (LLMs) (e.g., ChatGPT) that are becoming increasingly prevalent.In principle, such tools could support the use of multiple languages in the scholarly communication ecosystem.Imagine a scenario where an author from Chile submits a manuscript to a journal in Spanish.The editor identifies a subject expert in Japan, who uses a translation tool to get a version in Japanese and then prepares their peer review feedback in Japanese.This goes back to the editor, who machine translates the feedback into Spanish for the author.Following revisions, the article is published in Spanish, but scholars in Greece, Egypt, Thailand, or elsewhere can in turn use translation tools to read the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.021 | 0.056 |
| Scholarly communication | 0.042 | 0.052 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".