Bibliographic record
Abstract
Si la fraduction d'Anik de Repentigny donne de belles s6quences, si e1le r6vdle une sensibilitd certaine d 1a langue d'Atwood, elle pdche souvent par un manque d'audace et surtout par la qualit6 chancelante de sa propre langue.Or n'est-ce pas ldL le principe de base de tout texte litt6raire et de toute traduction, quel parti pris que I'on adopte, qu'il soit 6crit dans une langue stre ?On regrette que les 6diteurs n'aient pas d6barrass6 le texte de ses sol6cismes ; la qualit6 globale de la haduction en aurait profit6.(Et puisqu'il est question du travail des 6diteurs, Que dire de la quatridme de couverfure, oir.on lit que la traductrice a < compl€t6 > un baccalaur6at et une maitrise en 6tudes frangaises, oi on nous apprend que ce recueil (( est sa premidre traduction publide en po6sie >>, dans une toumure qui laisse pour le moins perplexe ?Que sont les 6diteurs devenus ?)Margaret Atwood est un monument de la litt6rature canadienne et il faut une bonne dose de courage pour s'attaquer d la traduction de ses podmes.Ce courage est tout dr l'honneur d'Anik de Repentigny.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.138 | 0.057 |
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".