La traduction spécialisée à l’ère des nouvelles technologies : quel effet sur le texte de spécialité?
Bibliographic record
Abstract
The introduction of translation technologies, especially translation memory software, has had a significant impact on both the translator’s professional practice and the target text itself. Apart from the fact that he or she must translate in a non-linear fashion due to the design of translation memory systems, the translator is now called upon to increase output and, in many cases, recycle what has already been translated by others. As a result, the translator, used to having full control over his or her text, is in some regards losing control over the translation process, which brings him or her to reflect on the quality of the final product and, in turn, on the transformations the field of specialized translation is undergoing. In this paper, I will present the results of an important ethnographic study conducted in three Canadian translation environments. I will focus mostly on the effects translation technologies and newly implemented practices have had on the quality of specialized texts destined for the Canadian market, where most of the specialized texts produced in French are in fact translations. Special attention will be given to the comments made by specialized translators during semi-directed interviews.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".