Bibliographic record
Abstract
This article sets out to show that translation, far from being a mere impersonation, is an activity involving a complex form of rational empathy. In order to do their work to the best of their abilities, translators must adopt an empathetic attitude not only toward the author of the source text, but also toward the text and the future reader whose knowledge skills he needs to consider in the same way the original author did. Our text situates the translation process in a functionalist perspective and subordinates it to the preliminary establishment of a translation postulate (“postulat traductif”) in accordance with which the translator lays down a translation strategy based on the type of text, the origin of the text, for whom the text is intended as well the function of the text. At this stage, the translator seeks to achieve maximum objectivity based on rational empathy. As a rule, empathy in translation is associated with literary translation, in keeping with a lyrical view of the latter and with an inordinate focusing on the author. We will try to show that the empathy factor is a constant, whether the text be literary or pragmatic, and that rational empathy is an indispensable tool for all translators since few of them are in a position to both choose their texts and hope to feel a spontaneous emotional empathy with their author or text. Lastly, we will see that a broad general culture, including both a knowledge of the source language and culture, combined with an acute awareness of one’s own culture, is an essential basis for rational empathy, and conditions the intercultural and communication skills of the translator. Seen from this angle, the translator must master the various linguistic codes, namely language levels and diatopic variations, which he must use in order to best fulfil his role as a translator according to the most efficient translation postulate.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".