Social justice and translator training and education in a time of (non-)equitable tech
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Social justice refers to the equitable distribution of resources, opportunities, and rights in society. Social justice frameworks acknowledge that structural inequalities can hinder accessibility to education and that the use of and recourse to technology is not always ethical or equitable. As translator trainers with more than two decades of experience in higher education, we reflect on the nexus between technology, translator training, ethics, and social justice, and put forward a list of strategies with which to humanize translator training or education and professional practice. We focus on Canada and draw from both a literature review using the Translation Studies Bibliography which shows that research on the subject of translator training or education and social justice is currently underdeveloped. We also draw from two media scans (conducted from November 2022 to October 2023) on the topics of higher education, translator training, social justice, and technological disruption and related digital divides, with a specific focus on machine translation and artificial intelligence. Along with other demolinguistic data from the 2021 Canadian Census, the review and scans contextualize the list of pedagogical recommendations and strategies we propose. We adopt the position that social justice should take precedence in the way we think about translator training and develop curricula rather than allowing market forces and the tech industry to determine training priorities and objectives. Artificial intelligence and other new(er) technologies can have pedagogical merit in higher education and in translator training, but it is imperative that we consider how and when to use the tools and to focus on issues that go beyond plagiarism and student surveillance. We therefore argue in favour of human, humane, and humanising translation in and beyond Canada and this means advocating and developing pedagogical strategies and curricula that align with the ethos of social justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it