Traduire le terme oppidum : un exemple des difficultés posées par le Bellum ciuile de César
Bibliographic record
Abstract
Il n’est pas ici envisagé de revenir en détail sur le terme oppidum et sur sa définition, mais plutôt de voir comment dans le cadre d’une œuvre, le Bellum ciuile de César, il a été employé. C’est principalement à partir de son emploi dans le Bellum Gallicum que les analyses ont jusque-là porté. Aussi, le contexte d’utilisation par l’auteur, dans une œuvre postérieure à la guerre des Gaules, conduit d’une part à s’interroger sur le choix du terme oppidum par César, et d’autre part à proposer des traductions différentes du mot ; c’est à travers plusieurs exemples que je souhaite évoquer la complexité de la démarche du traducteur.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".