Blick in die Black Box: Kreative Momente im Übersetzungsprozess: eine experimentelle Studie mit Translog
Bibliographic record
Abstract
Dans cet article, le processus de traduction est pris en considération sous l’angle des sciences cognitives. L’accent sera mis sur un élément essentiel de l’opération traduisante et qui pour cette raison mérite être étudié de manière plus détaillée et approfondie : il s’agit de lacréativitéen tant qu’activité servant à résoudre des problèmes de traduction. Mais qu’est-ce que la créativité en traduction exactement ? Quels sont les éléments qui la composent ?Quand, à savoir à quels moments durant le processus de traduction, etcomment, en d’autres termes, par quels facteurs, ce phénomène est-il produit et activé ? Voici seulement quelques questions, auxquelles nous allons tenter de répondre dans le cadre de notre étude ainsi que dans cette contribution.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 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".