Bibliographic record
Abstract
Une méthode rigoureuse d'évaluation des traductions est nécessaire à l'historien tout comme au pédagogue de la traduction. En nous inspirant des travaux théoriques d'Henri Meschonnic, nous tenterons de démontrer que l'évaluation des traductions du passé — des textes littéraires principalement — ne saurait se faire à partir des règles édictées par les traducteurs-théoriciens auteurs de traités sur la manière de traduire, et que l'analyse philologique et la linguistique différentielle ne suffisent pas non plus pour apprécier la réussite ou l'échec d'un texte traduit. L'historien de la traduction cherchera plutôt à savoir si l'œuvre traduite a l'historicité de l'œuvre originale, si la traduction-recréation a inventé sa propre poétique et remplacé les problèmes de langue par des solutions de discours. Traduire uniquement le sens d'une œuvre comporte le risque d'escamoter sa littérarité et sa poétique, ce qui aboutit à la production d'un non-texte.
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 | 0.046 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".