Ethics and crisis translation: insights from the work of Paul Ricoeur
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the ethical dimensions of crisis translation through the lenses of Paul Ricoeur’s philosophical scholarship. In particular, his work on both translation and ethics will be examined in order to draw practical applications for those involved in humanitarian action. Design/methodology/approach The authors identified relevant themes in the work of renowned philosopher Paul Ricoeur and used philosophical analysis to apply them to ethical issues in crisis translation. Findings Paul Ricoeur was one of the leading philosophers in the twentieth century, writing on a wide variety of topics. From these, his work on translation and on ethics provided suitable ways to examine ethical issues in crisis translation. In particular, his concept of “linguistic hospitality” provides an important lens through which translation ethics can be examined. In addition, Ricoeur’s approach to ethics emphasised relational and justice dimensions which are crucial to examine in humanitarian settings. Practical implications While the findings are conceptual, they have many practical implications for how translation is approached in humanitarian crises. The focus on justice in Ricoeur’s approach has implications for policy and practice and serves to ensure that translation is available for all affected communities and that all groups are included in discussions around humanitarian responses. Social implications Ricoeur’s work provides important insights into both translation and ethics that have significant social implications. His ideas highlight the personal and emotional aspects of translation and ethics, and point to their relational character. His openness to others provides an important basis for building trust and promoting dignity even in difficult humanitarian settings. Originality/value Ricoeur’s ethics points to the importance of persons and their relationships, reminding responders that translation is not just a mechanical exercise. This approach fosters an interest in and openness to others and their languages, which can promote respect towards those being helped in humanitarian crises.
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 | 0.038 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.073 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".