The Anglocentric nature of scholarly communication has many implications, such as limiting publication, discoverability and access from other language communities (even for major languages); putting minoritized languages at risk in the academic domain; and excluding many from peer review. The OSCAIL project addresses these challenges by exploring how machine translation (MT) enhanced by large language model (LLM)–based technologies can support access to scientific knowledge. Outputs will include evaluation datasets, protocols and best practices for MT in scholarly communication, and a prototype integration of MT tools into Open Journal Systems, the world’s most widely used open-source scholarly publishing platform.

AI-based technologies have disrupted the translation profession. While surveys such as the annual European Language Industry Survey have provided regular reports on the situation in Europe, less is known about the effects of AI-based translation tools on professional translators working elsewhere. This study reports on a survey that was conducted with support from the professional translators association of Quebec in Canada (Ordre des traducteurs, terminologues et interprètes agréés du Québec). One hundred and seventy-five completed surveys, along with additional partial responses, were analyzed. This article reports on questions relating to two broad categories: general perceptions about AI’s influence on the translation profession, and the evolution and sustainability of the profession. Where relevant, the results of this survey are compared to those from other parts of the world. Findings show that while Quebec translators face many issues that are similar to those faced by translators in regions such as the United Kingdom, France, Belgium, Switzerland, and Europe more generally, there are subtle differences also, such as the tendency of many Quebec translators to work as generalists, the comparatively low number of Quebec translators working in the Entertainment, Arts and Culture domains (which are showing signs of growth elsewhere), and the large number who seem hesitant to supervise interns moving forward.

Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability.This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.