Bibliographic record
Abstract
Translation and multilingualism are often associated with social justice, for translation breaks down communication barriers and multilingualism indexes inclusivity. Angermeyer challenges the assumption that translation and multilingualism necessarily advance social justice by pointing out the context dependence of their contribution. Not only are translation and interpreting not always an effective remedy to linguistic inequality, translation and interpreting practices can themselves be a source of such inequality. Angermeyer posits that interpreting practices can be discriminatory when they are provided in ways that prioritize the needs of the institution over those of users who are served by it, pointing to asymmetrical interpreting modes in institutional interpreting as evidence. He also demonstrates that an act of inclusivity could itself be discriminatory—for example, multilingualism could be used punitively to enforce stereotypes by singling out speakers of certain languages as potential offenders of public order. This response paper complements and complicates Angermeyer's intervention. While sharing concerns about problems that arise from certain modes of court interpreting and about the punitive use of multilingualism, this paper invites consideration of wider contexts, including different factors that affect the delivery of a fair trial and the role of private actors in shaping a linguistic landscape. It also highlights some recurring conflicts and gaps in the discussion of linguistic justice.
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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 | 0.027 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.114 |
| Scholarly communication | 0.021 | 0.038 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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".