Translation as Social Justice: Translation Policies and Practices in Non-Governmental Organisations (Book Review)
Bibliographic record
Abstract
For decades, a convincing body of scholarly literature has elicited the deeply embedded value of non-governmental organisations (NGOs) in upholding social justice, ensuring equity and access, and achieving sustainable development, which traverses geographical boundaries, language, and cultural barriers.However, the link between NGOs' use of language translation and interpreting (T&I) and their operational goals of social justice has been "largely overlooked" (p.2).Such disregard may magnify the potential gaps in communication when the default use of the dominant language (e.g., English as lingua franca) silences less-heard local language communities, particularly in peace-building and other post-conflict scenarios.The neglect of local or minority language communities may further impinge on the principles of inclusion and human rights in international development and humanitarian settings, despite the overarching mission to leave no one behind (United Nations, 2015, 2022).To bridge potential communication gaps, Tesseur's insightful monograph is written with two key guiding questions: (1) what can international non-governmental organisations (INGOs) do to embed a more linguistically attuned approach in their translation work?and (2) what can translation researchers do to ensure more socially just language and translation practices?With these questions in mind, Tesseur highlights traditionally ignored aspects of NGOs' language work as a tool of empowerment in facilitating multilingual communication, addressing longstanding cultural colonisation
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".