Bibliographic record
Abstract
Translation is usually deemed to help bridge gaps but seldom thought of as a means of strengthening or, at least, highlighting borders. The present article uses the example of translations involving the Breton language in order to show that translation may favour negotiation by both helping negotiators to understand each other and having them recognise the social border that makes them different. The article explains firstly the author’s understanding of borders and negotiation. Secondly, the case of translation from and into Breton is examined. And finally, the discussion is extended to the European institutions, where European language policy also illustrates the dual function of translation in negotiation. The example of Breton evidences that translation fosters social distinction, language development and cooperation. At the EU level, the same roles are assumed by translation services and they contribute moreover to the legitimacy of the institutions and to the exercise of democracy. Such a conclusion invites to consider translation as an adequate means to manage language and cultural differences, even compared to language learning. It may be used, then, to deal with pressing issues such as the current migration flows to Europe.
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 | 0.054 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.082 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".