Lynne Bowker and Jairo Buitrago Ciro. Machine Translation and Global Research: Towards Improved Machine Translation Literacy in the Scholarly Community. Bingley, Emerald Publishing, 2019, 111 p.

This book, by a translation scholar and a library scientist, aims to promote the intriguing notion of “machine translation literacy” (MT literacy) among researchers in all fields (not specifically Translation Studies), and also among librarians (academic, school and public), professors of library and information science, abstractors and indexers, peer reviewers, journal editors, publishers, and translators/editors who deal with research. The authors define MT literacy (p. 88) as understanding the basics of how MT works and how it can be used to find, read and write scholarly publications; understanding the wider implications of using MT; and having the abilities to evaluate how MT-friendly a text is, to write or modify a text so as to make it MT-friendly, and to edit MT outputs to improve their accuracy and readability. Chapter 1 looks at the rise of English as an international language of scholarly communication, and the various options open to non-native readers and writers of that language who need to publish in English. The authors talk about non-native speakers. However, there are many scholars who speak English as an additional language quite well but can’t write scholarly articles in that language well enough for acceptance. In this regard, I was not sure why they mention the colloquialization of academic writing in English over the past 30 years (pp. 60-61). This may well make reading easier for native readers, but probably makes it harder for those non-native readers whose mastery of the spoken language is weaker than their mastery of the written form. Both conditions exist: strong writing/weak speech and the opposite. Options (not all of which may be available to a given researcher) include publishing in a language other than English (but then the article will not be as widely read and may not count as much for career advancement purposes), improving one’s English (very time-consuming), recourse to professional translators (very expensive) or editors (fairly expensive), asking a language teacher at one’s university to translate or edit (this will be problematic if the colleague lacks subject-matter knowledge), asking a colleague in the discipline to translate or edit (the colleague may want to be credited as an author and, I would add, may not be very good at translating/editing), and approaching an online translation service in the gig economy (very cheap but the quality is likely to be poor, often just lightly edited MT output). The final option is machine translation into English of a researcher’s writing in their own language. The machine output can then be edited by a professional ‘post-editor’ or by someone in the discipline, either the researcher or an Anglophone colleague (who, according to some evidence, does not need to be familiar with the source language). The authors focus on self-post-editing by the researcher. They report (p. 26) a study in which researchers having little experience writing in English, and not very confident in that language, obtained (in their own opinion) better results when they self-post-edited machine translations into English from their own language than when they wrote directly in English. In that study, a professional reviser did not have to make more changes in the self-post-edited texts than in the texts written in English. However another study showed that not all necessary changes were made by self-post-editors who had no training in post-editing. Next, the authors look at the use of MT during the literature search at the outset of a research project. The first problem is finding relevant material in English. The authors conducted an experiment (pp. 28-29) in which they used MT to translate Spanish keywords in a Spanish-only library science journal into English. …