Translation as discrimination: Sociolinguistics and inequality in multilingual institutional contexts
Bibliographic record
Abstract
Abstract Sociolinguistic approaches to social justice tend to treat the use of interpreters or translators as a remedy to linguistic inequality in multilingual institutional settings. This article challenges this assumption by showing how translation can instead contribute to inequality and discrimination. Drawing on studies of face-to-face interpreting in judicial contexts and of written translation in linguistic landscapes, it explores inequalities found in habitual practices of professional interpreters and in the use of machine translation. It shows how language ideologies about multilingualism motivate translation practices that systematically restrict the participation of speakers of subordinated languages, or that stereotype them as deviant when addressed solely by prohibitions and warnings, a practice I call ‘punitive multilingualism’. The article thus argues that sociolinguistic studies of multilingualism should pay closer attention to translation practices within a wider context of language contact and in relation to phenomena such as translanguaging, mock languages, or language shift. (Translation, interpreting, justice, linguistic landscape, discrimination)*
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.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.034 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".