Ethics at the Intersection of Crisis Translation and Humanitarian Innovation
Bibliographic record
Abstract
Language and its translation are important operational concerns in humanitarian crisis response. Information sharing, coordination, collaboration and relationship-building all revolve around the ability to communicate effectively. However, doing so is hampered in many humanitarian crises by linguistic differences and a lack of access to adequate translation. Various innovative practices and products are being developed and deployed with the goal of addressing these concerns. In this theoretical paper, we critically appraise the ethical terrain of crisis translation and humanitarian innovation. We identify ethical issues related to three broad themes. First, we foreground questions of justice in access to translation and its prioritisation in contexts of widespread and pressing needs. Second, we consider the relationship between humanitarian ethics and the ethics of crisis translation. We argue for the importance of attending to epistemic justice in humanitarian crisis response, and consider how Ricoeur’s conception of linguistic hospitality provides insights into how relationships in humanitarian settings can be understood through the lens of an ethics of exchange while also acknowledging the steep asymmetries that often exist in these contexts. Finally, we identify issues related to how translation innovations intersect with humanitarian values and humanitarians’ ethical commitments.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.112 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".