Boucler la boucle : réflexions sur un trajet traductologique en spirale
Bibliographic record
Abstract
Cet article retrace le cheminement intellectuel de l’auteure, depuis la fondation de l’Association canadienne de traductologie jusqu’au présent, évolution qui reflète, en quelque sorte, celle de la discipline émergente de la traductologie en général. Depuis le premier article qu’elle publie dans TTR, portant sur les traducteurs et écrivains, elle ne cesse de s’intéresser au sujet traduisant, ce qui débouche sur une histoire des traducteurs, suivie de différentes études d’éminents écrivains-traducteurs. L’article propose quelques mini-portraits de ces écrivains – Paul Valéry, Bernard Shaw, Gertrude Stein, Paul Auster, notamment – afin de faire ressortir leur motivation à traduire et les différentes façons dont ils conçoivent et manipulent la traduction.
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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.056 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".